Data Scientist ll - Digital Intelligence
Why Socure?
Socure is building the identity trust infrastructure for the digital economy — verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day.
We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won’t be your place. If you want to help build the future of identity with a team that holds a high bar for itself — keep reading.
Job Summary:
Socure is the leading provider of digital identity verification and fraud prevention solutions, using AI and machine learning to power accurate identity trust decisions. Our mission is to eliminate identity fraud and ensure online trust across industries.
We are seeking a Data Scientist II to join our Digital Intelligence team. In this role, you will develop machine learning features, analytical methods, and production-oriented risk signals using device, network, browser, mobile, API, session, and behavioral telemetry.
This is a hands-on role for a data scientist who can independently deliver well-scoped projects, work with complex and noisy data, and partner with engineering, product, and risk teams to improve fraud detection, identity confidence, and customer outcomes. You will deepen your expertise in Digital Intelligence while contributing to models and signals used in real-world production decisions.
Job Responsibilities:
Develop machine learning features, models, and analytical methods for device, network, browser, mobile, session, and behavioral intelligence.
Work on scoped fraud and identity risk problems where data quality, labels, telemetry coverage, and product tradeoffs need careful analysis.
Build features from large-scale, high-cardinality, sparse, noisy, and platform-dependent telemetry.
Analyze signal patterns such as spoofing, emulator behavior, automation, proxy/VPN usage, low-entropy fingerprints, telemetry gaps, and device or session fragmentation.
Design and execute validation analyses, including train/test splits, holdout checks, leakage review, drift assessment, customer impact analysis, and feature stability review.
Use supervised, unsupervised, statistical, and heuristic approaches to identify durable fraud and identity risk signals.
Investigate imperfect labels, delayed outcomes, instrumentation gaps, and changing fraud patterns to distinguish useful signal from data artifacts.
Partner with senior data scientists, engineering, product, risk, and platform teams to clarify requirements, prepare data, implement features, and support production rollout.
Contribute to model documentation, feature definitions, explainability materials, dashboards, and production-readiness reviews.
Communicate methods, assumptions, findings, limitations, and recommendations clearly to technical and cross-functional stakeholders.
Support junior data scientists and analysts through code review, analytical feedback, and sharing effective modeling and validation practices.
Job Requirements:
Bachelor’s, Master’s, or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, Data Science, or a related quantitative field, or equivalent practical experience.
5+ years of experience in data science, applied machine learning, statistical modeling, analytics engineering, or a related technical role.
Experience building, evaluating, and improving machine learning models, features, analytical pipelines, or risk signals.
Strong SQL skills and experience working with large-scale, complex datasets.
Strong proficiency in Python and experience with data science libraries such as pandas, NumPy, scikit-learn, XGBoost, TensorFlow, PyTorch, or similar.
Experience with distributed data processing tools such as Spark, PySpark, Databricks, or equivalent frameworks.
Solid understanding of supervised learning, unsupervised learning, feature engineering, model evaluation, statistical validation, and experiment analysis.
Ability to work with noisy data, imperfect labels, missing values, instrumentation gaps, and changing data distributions.
Strong analytical judgment across data quality, feature design, model selection, explainability, and business impact.
Experience collaborating with engineering, product, analytics, or risk teams to move data science work toward production or operational use.
Clear communication skills, including the ability to explain technical work, assumptions, tradeoffs, and results to non-specialist stakeholders.
Ability to operate independently on defined problem areas while seeking guidance appropriately on ambiguous or high-risk decisions.
Preferred Qualifications:
Background in fraud detection, identity verification, trust and safety, anomaly detection, cybersecurity, risk modeling, or another adversarial data domain.
Experience with device intelligence, browser/mobile fingerprinting, behavioral biometrics, network intelligence, VPN/proxy detection, or telemetry signal processing.
Experience developing features from high-cardinality categorical data using techniques such as aggregation, frequency encoding, target encoding, embeddings, graph features, or representation learning.
Familiarity with production ML workflows, model monitoring, feature monitoring, or batch and near-real-time decisioning systems.
Experience with dashboarding, model explainability, feature documentation, or customer-impact analysis.
Interest in adversarial behavior, fraud patterns, telemetry quality, and applied ML systems that operate in real-world production environments.
What You’ll Gain
You will work on meaningful data science problems in fraud prevention and identity verification, using high-scale Digital Intelligence telemetry to build features and risk signals that contribute to real-world production decisions.
You will gain deeper experience with device, network, browser, mobile, session, and behavioral intelligence while working closely with senior data scientists, engineering, product, and risk partners. This role offers the opportunity to grow from independently delivering scoped modeling projects toward owning broader workstreams and developing Senior-level technical judgment over time.
Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
If you need an accommodation during any stage of the application or hiring process—including interview or onboarding support—please reach out to your Socure recruiting partner directly.
Follow Us!
YouTube | | X (Twitter) | Facebook
Senior Data Scientist - Big Data R&D, Identity Graph & KYC
Why Socure?
Socure is building the identity trust infrastructure for the digital economy — verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day.
We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won’t be your place. If you want to help build the future of identity with a team that holds a high bar for itself — keep reading.
About the Role
The Big Data R&D team develops cutting‑edge big data and graph‑based solutions for entity search, entity resolution, and identity matching that power Socure’s KYC and compliance products.
As a Senior Data Scientist I, you will lead the design and deployment of advanced ML and graph algorithms on large-scale PII datasets, own end‑to‑end projects from problem definition through production validation, and serve as a key technical partner to Product, Engineering, and Client‑facing teams. You will help define standards for feature engineering, experimentation, and data quality across our identity graph stack, with substantial impact on coverage, accuracy, and fairness.
What You'll Do
Own the design, development, and evaluation of machine learning, statistical, and graph-based algorithms for entity-resolution, identity trust scoring, and anomaly detection on massive datasets.
Architect and optimize graph-based identity representations (identity graph structure, linkage rules, clustering) to improve match rates, reduce false positives/negatives, and support downstream fraud and KYC models.
Build and maintain scalable data pipelines and feature stores in Spark/PySpark (or Scala), including data normalization, deduplication, and feature computation across large PII datasets in AWS/Databricks environments.
Lead A/B tests and offline/online experimentation for new models, features, and data sources; define success metrics, design experiments, and ensure rigorous validation before rollout.
Evaluate new internal and external data sources: explore signal quality, design backtests, quantify incremental value, and provide clear recommendations on vendor selection and integration.
Partner closely with product managers and engineers to translate ambiguous business and regulatory requirements (e.g., KYC coverage, watchlist matching) into concrete modeling and data roadmaps.
Provide deep analytical support to Socure’s compliance and regulatory product suite, including investigative analyses, root‑cause analysis for anomalies, and clear narratives for internal and external stakeholders.
Contribute to model governance and documentation: clearly explain model logic, data dependencies, limitations, and monitoring plans to internal risk/compliance stakeholders.
Mentor junior data scientists and engineers on best practices in data exploration, feature engineering, experimentation, and code quality.
Communicate complex technical concepts and trade‑offs in a concise, structured way to both technical and non‑technical audiences (e.g., product reviews, customer meetings, internal briefings).
What You Bring
Master’s degree with 3+ years of relevant industry experience, or Ph.D. with 1+ years of experience in applied ML / data science roles; background in Computer Science, Statistics, Mathematics, or related quantitative fields preferred.
Strong proficiency in Python (preferred) or Scala, including experience with ML libraries such as scikit‑learn, XGBoost, TensorFlow or PyTorch.
Extensive experience with Spark or PySpark and distributed data systems (e.g., AWS EMR, Databricks) working on very large, messy datasets.
Deep understanding of supervised and unsupervised learning, feature engineering, model evaluation, and experiment design (A/B testing, holdout strategies, stratification).
Experience developing production-quality data pipelines and automated workflows using Airflow or similar orchestration tools.
Practical familiarity with graph databases and/or graph frameworks (Neo4j, AWS Neptune, GraphFrames, DGL, PyTorch Geometric) and graph algorithms for clustering, link prediction, and community detection is strongly preferred.
Solid SQL skills and experience working with large-scale analytical data stores.
Experience in at least one of: identity verification, fraud detection, credit risk, or adjacent high‑stakes domains is a plus.
Demonstrated ability to lead medium‑to‑large projects end‑to‑end, make sound trade‑off decisions under ambiguity, and influence cross‑functional stakeholders with data and clear reasoning.
Please note that sponsorship is not available at this time; and that you must be located within 45 miles of a talent hub to be considered.
Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
If you need an accommodation during any stage of the application or hiring process—including interview or onboarding support—please reach out to your Socure recruiting partner directly.
Follow Us!
YouTube | | X (Twitter) | Facebook
Senior Data Scientist - International eKYC, Identity Graph
Why Socure?
Socure is building the identity trust infrastructure for the digital economy — verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day.
We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won’t be your place. If you want to help build the future of identity with a team that holds a high bar for itself — keep reading.
About the Role
The Big Data R&D team builds the core entity‑resolution and graph‑based intelligence that underpins Socure’s Verify and KYC products. As a Senior Data Scientist focused on international eKYC, you will be a technical leader driving the next generation of global identity verification solutions. You will design and deploy ML and graph-based systems tailored to diverse international markets, regulations, and data ecosystems—covering government IDs, telco and credit bureaus, mobile-first data, and non‑traditional signals.
You will own complex, cross‑product initiatives such as international identity graph evolution, probabilistic matching for non‑US identities, and scalable evaluation frameworks that account for regional regulatory and fairness constraints. You will closely partner with Product, Engineering, Compliance, and GTM teams to launch and scale eKYC solutions across multiple countries and regions.
What You'll Do
International eKYC Modeling & Entity Resolution
Lead the design, development, and deployment of ML and graph-based algorithms for international entity resolution, identity trust scoring, and anomaly detection across heterogeneous, country‑specific datasets.
Architect reusable matching and linking frameworks that work across multiple ID schemes (e.g., national ID numbers, passports, voter IDs, mobile accounts, bank accounts) and local name/address conventions.
Develop probabilistic and rule‑augmented models that handle noisy, sparse, or partially labeled international data while maintaining explainability and regulatory defensibility.
Global Identity Graph & Data Quality
Define and evolve the international extension of Socure’s identity graph: schema design, linkage strategies, quality tiers, and confidence scoring that can be leveraged by multiple products (Verify, KYC, watchlists, fraud).
Design and implement robust data quality and monitoring frameworks for international identity data (coverage, stability, drift, regional bias, label quality) and integrate them into modeling and production monitoring workflows.
Build scalable approaches for handling linguistic and cultural variation (e.g., transliteration, multi‑script names, address normalization, local naming patterns) in the identity graph and matching pipelines.
Evaluation, Experimentation, and Model Governance
Own experimentation strategy for major international eKYC initiatives:
Design offline evaluations and online A/B tests that reflect local ground truth constraints and data sparsity.
Define success metrics that balance approval rates, fraud capture, and regulatory/operational constraints per market.
Analyze lift, stability, and fairness trade‑offs and drive go/no‑go decisions with Product and Engineering.
Define and maintain evaluation frameworks specific to international eKYC (e.g., regional coverage maps, cross‑border identity leakage, local demographic impact, regulatory thresholds).
Contribute to model governance documentation and support responses to regulators and large enterprise customers regarding model logic, data provenance, fairness, and monitoring for international markets.
Data Source Strategy & Vendor Evaluation (International)
Lead the evaluation and integration of international data vendors (e.g., bureaus, telcos, public records, alternative data):
Design benchmarking methodologies for signal quality, incremental value, stability, and fairness by country/segment.
Quantify ROI and trade‑offs across multiple vendors and data types; provide clear recommendations that influence product and commercial decisions.
Partner with Data Acquisition, Legal, and Compliance to ensure that data usage and modeling approaches meet regional regulatory requirements (e.g., GDPR and local privacy/AML/KYC rules).
Technical Leadership & Cross‑Functional Partnership
Collaborate with engineering leaders to design scalable, reliable international data and model pipelines using Spark/PySpark, AWS (EMR, S3, SageMaker, Neptune), and modern MLOps workflows.
Act as a subject‑matter expert on international identity, eKYC regulations, and cross‑border data limitations for internal stakeholders, supporting complex customer questions and strategic roadmap discussions.
Mentor Data Scientists and Senior Data Scientists on best practices for international modeling: handling low‑label regimes, domain adaptation, localization of thresholds/logic, and building reusable abstractions instead of one‑off country fixes.
Communicate strategy, progress, and results to senior leadership and cross‑functional partners through clear documents and presentations, framing complex technical work in terms of business impact, regional risk, and regulatory trade‑offs.
What You Bring
Education & Experience
Master’s or Ph.D. in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, or a related field, or equivalent practical experience.
6+ years of hands-on applied ML / data science experience (4+ with Ph.D.), including owning production models and pipelines in high‑stakes domains (fraud, risk, identity, payments, credit, or similar).
Significant prior work on international or multi‑region products is strongly preferred (e.g., cross‑country KYC, credit risk, payments, or compliance systems).
Technical Skills
Expert‑level proficiency in Python and SQL, with extensive experience in distributed data processing (Spark/PySpark, Databricks or similar) on very large datasets.
Deep experience designing, training, and deploying models for classification, ranking, anomaly detection, and/or graph learning, including:
Feature engineering for noisy/heterogeneous identity data.
Robust evaluation under label sparsity and feedback delays.
Calibration and thresholding tailored to regional risk and regulatory constraints.
Proven expertise with graph technologies (e.g., Neo4j, AWS Neptune, GraphFrames, DGL, PyTorch Geometric) and graph algorithms (entity resolution, link prediction, community detection, label propagation) at scale.
Please note that sponsorship is not available at this time; and that you must be located within 45 miles of a talent hub to be considered.
Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
If you need an accommodation during any stage of the application or hiring process—including interview or onboarding support—please reach out to your Socure recruiting partner directly.
Follow Us!
YouTube | | X (Twitter) | Facebook
Data Scientist II - Big Data R&D, Identity Graph & KYC
Why Socure?
Socure is building the identity trust infrastructure for the digital economy — verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day.
We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won’t be your place. If you want to help build the future of identity with a team that holds a high bar for itself — keep reading.
About the Role
The Big Data R&D team is responsible for building the core identity graph and entity-resolution capabilities that power Socure’s KYC and compliance products. In this role, you will help develop graph-based algorithms and data pipelines on massive PII datasets, support modelers with high-quality features, and evaluate new data sources that feed our identity and fraud products. You will work closely with senior data scientists and engineers while developing your skills in large-scale ML, distributed systems, and graph analytics.
What You'll Do
Contribute to the design and implementation of machine learning, data mining, statistical, and graph-based algorithms to analyze very large datasets for identity verification and anomaly detection.
Analyze large datasets to help develop and refine entity-resolution and identity-matching algorithms that drive Socure’s KYC and compliance solutions.
Build and maintain components of data-processing pipelines (ETL, feature generation, normalization) using tools such as Spark/PySpark and AWS (e.g., EMR, S3).
Support senior data scientists with feature engineering, data exploration, error analysis, and A/B test setup for new models and signals.
Help evaluate new third‑party and internal data sources: profile data quality, design offline experiments, and summarize impact on coverage and model performance.
Implement and maintain SQL and Python/R code for data extraction, transformation, and validation; contribute to code reviews and basic testing.
Provide analytical support to compliance and regulatory product teams, including ad hoc investigations, simple dashboards, and data deep dives.
Communicate findings in a clear, structured way to peers and cross‑functional partners (Product, Engineering, Client Analysis), focusing on key insights and trade‑offs.
Work effectively in a fast‑paced, cross‑functional environment; demonstrate ownership of well-scoped tasks and follow through to completion.
What You Bring
Master’s degree with 2+ years of experience, or Ph.D. with 1+ years of experience in a data science or analytics role, or equivalent practical experience.
Proficiency in at least one general-purpose programming language used in data science (Python, or Scala).
Solid experience writing and optimizing SQL for large datasets; comfort working in data lake / warehouse environments.
Hands‑on experience with Spark or PySpark and common ML libraries (e.g., scikit‑learn, XGBoost, TensorFlow/PyTorch a plus).
Familiarity with UNIX environments and the AWS ecosystem (e.g., EMR, S3); Databricks experience is a plus.
Working knowledge of supervised/unsupervised ML and basic statistics (similarity measures, clustering, evaluation metrics).
Exposure to graph techniques or graph databases (Neo4j, AWS Neptune, GraphFrames) is a strong plus.
Bonus: experience with Elasticsearch or DynamoDB; workflow tools such as Airflow for automating data pipelines.
Ability to break down loosely defined problems, ask good clarifying questions, and iterate quickly with feedback.
Please note that sponsorship is not available at this time; and that you must be located within 45 miles of a talent hub to be considered.
Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
If you need an accommodation during any stage of the application or hiring process—including interview or onboarding support—please reach out to your Socure recruiting partner directly.
Follow Us!
YouTube | | X (Twitter) | Facebook
Staff Data Scientist - RiskOS
Why Socure?
Socure is building the identity trust infrastructure for the digital economy — verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day.
We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won’t be your place. If you want to help build the future of identity with a team that holds a high bar for itself — keep reading.
About the Role
Socure is the leading provider of digital identity verification and fraud prevention solutions, leveraging AI and machine learning to power the most accurate decisions. Our mission is to eliminate identity fraud and ensure online trust across industries.
As a Staff Data Scientist for RiskOS, you will sit at the intersection of platform data science, fraud and risk analytics, and Generative AI. You will own end‑to‑end development of data‑driven solutions on the RiskOS platform—from heavy‑duty data exploration and cleaning, through modeling and GenAI agent design, all the way to production deployment and monitoring.
You will leverage your expertise in fraud and risk management to help develop and integrate robust detection and decisioning models, and your experience with Generative AI to design, evaluate, and operationalize LLM‑powered tools that improve analytics, workflows, and case investigations.
You will collaborate closely with engineering and platform teams to build scalable, production‑grade pipelines and services, and with product and risk leaders to ensure RiskOS delivers actionable insights, self‑serve analytics, and best‑in‑class fraud prevention at scale.
This is a highly collaborative, hands‑on technical leadership role for someone who enjoys owning complex data problems end‑to‑end and acting as a force multiplier for other data scientists and product teams.
What You'll Do
Develop and implement advanced analytics on top of noisy, heterogeneous RiskOS data to understand user behavior, product usage, fraud patterns, and workflow effectiveness; translate findings into concrete product and risk strategy improvements.
Architect and build scalable data pipelines and production ML workflows, collaborating with data engineering to ensure robust, reliable, and efficient data processing for both batch and streaming use cases.
Lead the design, execution, and analysis of experimentation frameworks to optimize user journeys, feature adoption, and workflow performance across the RiskOS platform.
Lead the creation and evaluation of Generative AI solutions (LLMs, agents, prompt‑based tools) that automate analytics, power case review and investigation assistants, streamline documentation, and enhance RiskOS workflows and reporting.
Define rigorous evaluation frameworks for GenAI solutions, including offline benchmarks, human‑in‑the‑loop review, safety and hallucination checks, and impact measurement in production.
Partner with platform and engineering teams to define and build core RiskOS data science infrastructure, including feature stores, model‑serving APIs, evaluation services, and monitoring frameworks for both traditional ML and GenAI systems.
Own end‑to‑end deployment of production‑grade solutions: packaging models and GenAI workflows, integrating with RiskOS services, establishing SLAs, and instrumenting telemetry, alerting, and feedback loops.
Develop and automate tools for model evaluation, stress testing, backtesting, and adversarial scenario simulation to ensure robustness and operational resilience—especially in high‑risk fraud and compliance contexts.
Enable product and risk teams through self‑serve analytics and tools: build dashboards, template analyses, and GenAI‑driven assistants that help non‑technical users explore RiskOS data, tune workflows, and debug decisions.
Collaborate cross‑functionally with product, engineering, risk, solution consulting, and customer‑facing teams to translate business requirements into data‑driven solutions and actionable insights, particularly for fraud and risk use cases on RiskOS.
Mentor and provide technical guidance to other data scientists and analysts, modeling best practices in experimentation, software engineering hygiene, GenAI safety, and rigorous model evaluation.
Ensure all solutions adhere to best practices in data privacy, security, and compliance, especially when handling sensitive PII and financial data in regulated fintech and public‑sector environments.
Contribute to company‑wide standards for ML and GenAI explainability, risk evaluation, feature logging, and documentation, helping raise the overall AI bar across Socure.
Communicate complex technical concepts and findings clearly to both technical and non‑technical stakeholders, including executive leadership and external partners.
What You Bring
Master’s or PhD in Computer Science, Machine Learning, Statistics, Engineering, or a related quantitative field, or equivalent professional experience.
6+ years of hands‑on experience in data science, machine learning, or high‑scale data engineering roles, with a proven track record in fraud prevention, risk analytics, or complex decisioning systems.
Strong experience applying Generative AI in production or near‑production contexts, including:
Building and evaluating LLM‑based applications or agents (e.g., retrieval‑augmented generation, workflow assistants, data‑insight copilots).
Prompt design and optimization, safety and guardrail techniques, and quantitative/qualitative evaluation of LLM outputs.
Deep proficiency in Python and SQL, with hands‑on experience using ML frameworks such as scikit‑learn, XGBoost, TensorFlow, or PyTorch, plus modern GenAI/LLM tooling (e.g., OpenAI/Anthropic APIs, Hugging Face ecosystems, orchestration frameworks).
Demonstrated experience building and maintaining scalable data pipelines and deploying ML models in production environments, ideally involving streaming or near‑real‑time data and modern data platforms (e.g., Databricks, Spark, PySpark, BigQuery, or similar).
Solid understanding of data engineering concepts, including ETL, data warehousing, schema design, and distributed computing.
Experience with platform‑oriented data science: working with feature stores, model‑serving infrastructure, CI/CD for ML, automated monitoring, and feedback collection workflows.
Hands‑on experience wrangling messy, high‑volume datasets: designing robust cleaning, normalization, and quality‑control processes; reasoning under missing or biased data; and building reusable data abstractions for other users.
Familiarity with privacy‑preserving ML techniques, secure data handling, and regulatory requirements in fintech, credit, or public‑sector environments is strongly preferred.
Proven ability to collaborate effectively in cross‑functional, fast‑paced teams; strong communication skills with comfort presenting trade‑offs and recommendations to senior stakeholders.
Product‑minded and outcome‑oriented: you care about how models and GenAI tools are used, how they shape user experience and risk posture, and how to measure their real‑world impact.
Preferred Qualifications
Direct experience with fraud/risk modeling, identity verification, or trust & safety.
Prior work on orchestration platforms, case‑management tools, or rules/decision engines.
Experience mentoring senior ICs and setting technical direction for a small data science group.
Please note that we are unable to provide sponsorship for this role; now or in the future.
Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
If you need an accommodation during any stage of the application or hiring process—including interview or onboarding support—please reach out to your Socure recruiting partner directly.
Follow Us!
YouTube | | X (Twitter) | Facebook
Head of Data & Operational Intelligence
Why Socure?
Socure is building the identity trust infrastructure for the digital economy — verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day.
We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won’t be your place. If you want to help build the future of identity with a team that holds a high bar for itself — keep reading.
The Opportunity
Roles like this exist at very few companies. The data is rich enough, the domain is complex enough, and the moment is consequential enough to build something that has not been built before. You will own data intelligence, customer health, data governance, real-time signal detection, and an AI agent layer simultaneously, across the company.
Socure sits at a rare intersection: a company with one of the richest proprietary datasets in enterprise software, a platform that processes identity and fraud signals at massive scale in real time, a consortium of customers whose collective behavioral patterns reveal things no individual institution could see alone, and a growth trajectory that requires the intelligence infrastructure to match. The person who takes this role will build and operate the system that makes Socure as smart about itself as it is about the identities it verifies.
The scope is broad by design. You will own four interconnected intelligence layers: the business and investor observability platform that produces a single authoritative answer to any strategic question the company needs to answer; a customer health intelligence framework that monitors product adoption, model performance, and configuration hygiene across every account; a real-time signal intelligence system that detects fraud attack patterns and behavioral anomalies across the full customer base before any individual customer can see them; and an AI agent layer that makes all of it accessible to anyone in the company in plain language, in under 30 seconds. You will report to the Head of Platform Services and will have the organizational mandate, the executive sponsorship, and the technical resources to build this from the ground up.
If you have spent your career building pieces of this at different companies and have wanted to build the whole thing at once, at a company where the data is genuinely interesting and the stakes are real, this is that role.
What You Will Build
Business and Investor Intelligence
Socure has one of the most compelling growth narratives in enterprise software. Vertical diversification is accelerating. International transaction volume is growing rapidly. The land-and-expand model is working: second deals close in a fraction of the time it takes to close the first, and expansion revenue now outpaces new logo revenue. You will build the authoritative data foundation that makes every one of those stories produceable on demand, from a single source, with metric definitions that are version-controlled and enforced at the data layer. The goal is a state where any investor question gets a verified answer in seconds with zero definitional conflicts across teams.
Customer Health Intelligence
A Socure implementation involves more than 3,000 individual product settings and flags distributed across RiskOS, SuperAdmin, and Control Center. No other platform in our competitive set has built product configuration hygiene explicitly into a customer health scoring framework. You will build it. The full scoring model spans model freshness, use case breadth relative to potential, volume trend against commitment, engagement quality, and configuration hygiene, computed weekly across every active account, with automated alerting when trajectory changes. The output is a structured intervention system that lets Customer Success and Account Management act before a customer has decided anything.
Customer Signal Intelligence and Anomaly Detection
In 2025, deepfaked selfies increased 58% year over year. Injection attacks surged 40%. Native Virtual Camera attacks grew 2,665%. The fraud attack surface is expanding faster than any individual customer can track independently. Socure's consortium data makes cross-customer pattern recognition possible in a way no single-institution team could replicate. You will build the real-time monitoring system that detects population-level behavioral anomalies, model input and output drift, and emerging fraud attack signatures across the full customer base, and delivers a structured incident brief to the right customer contact within 15 minutes of onset.
The AI Agent Layer
This is the part of the role that will define it. You will build a natural language intelligence interface powered by AI agents that sits above all three pillars. Any employee, in any function, should be able to ask a business question in Slack and get a verified, sourced, accurate answer in under 30 seconds. The multi-agent pipeline you design will handle query understanding, data retrieval, verification, and answer synthesis, with answer accuracy tracked as a first-class system metric. Socure already launched an AI Suite of Agents in RiskOS for our enterprise customers in October 2025. The internal intelligence layer you build applies the same architecture philosophy to how we run the company. The lessons from building it will directly improve the external product.
What We Are Looking For
You have built data platforms that operate at scale and you understand the full stack from ingestion and transformation to semantic layer and consumption. You are AI-native in the truest sense: agents and LLM-powered reasoning are core architectural instincts for you, not bolt-ons. You are as comfortable in a board prep session as you are in a technical design review, and you can move fluidly between those contexts without losing credibility in either.
You understand that the hardest part of this job is not the technology. It is the governance: getting a fast-moving organization to agree on canonical definitions and holding the line on them when the business wants to move faster than the data model can follow. You have done that before and you know what it takes.
Required
12+ years of engineering experience including significant time building or operating large-scale data platforms, data warehouses, and analytics infrastructure
5+ years leading engineering teams with company-wide scope and impact
Track record building enterprise data platforms at scale spanning transactional, CRM, and billing source systems
Deep fluency with modern data stack architecture: cloud data warehouses, data mesh and medallion patterns, semantic and metrics layers
Hands-on experience shipping AI agent systems including multi-agent orchestration, RAG pipelines, and LLM-powered natural language interfaces
Strong instincts around statistical process control, anomaly detection, and real-time streaming architectures
Data governance depth: metrics catalogs, lineage, ownership models, and version-controlled metric definitions
Experience translating board-level and investor-level business questions into data architecture requirements
Excellent communication skills with the ability to move fluidly between technical design and executive stakeholder conversations
Preferred
Deep experience with AWS data services including S3, Glue, Athena, Redshift, Lake Formation, EMR, Kinesis, and Lambda
Familiarity with modern data stack tooling such as dbt, Airflow, Spark, Fivetran, Databricks, SageMaker Unified Studio, or similar
Experience partnering with Finance and FP&A teams on revenue recognition, ARR/NRR analytics, and usage-based billing reconciliation
Experience supporting investor reporting, board reporting, or capital markets readiness in a high-growth SaaS environment
Background in fintech, identity verification, fraud, or similarly high-stakes data domains
Prior experience building data platforms that support regulated financial workflows including SOC 2, PCI or similar compliance regimes
Prior experience in usage-based or consumption-based SaaS
We have one of the most powerful datasets in enterprise software. We have the scale, the domain, and the architectural foundation to do something genuinely interesting with it. This role is how we do that.
Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
If you need an accommodation during any stage of the application or hiring process—including interview or onboarding support—please reach out to your Socure recruiting partner directly.
Follow Us!
YouTube | | X (Twitter) | Facebook
The full posting opens here — pay, setting and the full description, without leaving the list.