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.

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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

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