Description

Founded in 2002, AU10TIX is the global leader in AI-driven identity verification and management, protecting the world's largest brands against advanced fraud. The company's future-proof product portfolio helps businesses provide frictionless customer onboarding and verification in 4–8 seconds while staying ahead of emerging threats and evolving regulatory requirements.

We are looking for a sharp, data-driven Team Leader to lead our Fraud Operations team. The ideal candidate is an experienced analytical team leader who has led data analysts or fraud/risk operations professionals in a production-oriented environment. We are also open to exceptional individual contributors who have demonstrated clear leadership potential, strong ownership, and the ability to influence and guide others beyond their formal role.

You will own the team's research output, guide the shift toward more data-driven ways of working, and serve as the primary point of contact for a wide range of internal stakeholders, from day-to-day operational partners to senior leadership.

Key Responsibilities:

• Lead and develop the Fraud Operations team, fostering a data-driven, research-oriented culture
• Generate research-driven insights and oversee gap analyses to identify weaknesses in fraud detection processes and models
• Ensure delivery of high-quality, production-ready analytical updates with measurable impact
• Serve as the primary cross-functional interface with partner teams including R&D, BI, Product, Support, and Management
• Guide the team's adoption of AI-powered tools and modern analytical practices
• Engage confidently with senior leadership, maintaining focus and clarity under pressure

Requirements

• 3–6 years of hands-on experience as a Data Analyst, including proven leadership experience; prior experience managing or professionally leading analysts is strongly preferred, while exceptional senior individual contributors with demonstrated leadership potential will also be considered
• Proven ability to work responsibly in production-sensitive environments, remain composed under pressure, contain complex operational situations, and drive clear, practical resolution across teams
• Proficiency in Python and core data libraries (pandas, NumPy, visualization and stats packages)
• Strong SQL skills and experience working with large datasets in production
• Proficiency in leveraging LLM-based tools as part of day-to-day work
• Excellent interpersonal and communication skills
• Proactive problem-solver with a can-do approach
• Ability to influence without authority and drive alignment across teams
• Demonstrated ability to lead and develop team members, with a genuine passion for mentoring and knowledge sharing
• Confident and concise communicator with senior stakeholders

Nice to Have

• Experience in identity verification or fraud detection
• Familiarity with Elasticsearch
• Familiarity with AI agent frameworks and automation workflows

Description

Founded in 2002, AU10TIX is the global leader in AI-driven identity verification and management, protecting the world's largest brands against advanced fraud. Our platform verifies identities in 4-8 seconds while staying ahead of evolving threats and regulatory requirements. Behind that speed is a growing network of autonomous AI agents, and a program to scale AI-driven automation across the entire organization. You'll be building that next layer.

As a Threat Intelligence analyst, you will monitor the external threat landscape relevant to identity verification and fraud, and translate findings into actionable intelligence for AU10TIX teams.

What will you do:

• Continuous monitoring of fraud ecosystems and dark web marketplaces – tracking emerging forgery techniques, document fraud trends, and biometric attack methods (PAD, injection, deepfake-based)
• Analysing fraud patterns in AU10TIX production traffic- identifying emerging attack types, frequency trends, and gaps in current detection coverage
• Sourcing forged sample artifacts for the Red Team’s repository
• Producing threat intelligence reports and alerts for Product, Blue Team, and Legal, and maintaining a structured knowledge base of threat actors, tools, and TTPs
• Translating intelligence into prioritized test scenarios and capability gaps for the Red Team backlog

Requirements

• 2-4 years of experience with Open Web Intelligence methodology and tooling - source identification, verification, and pivoting across the open and deep web
• 2-4 years of experience working with SQL- ability to query and analyse fraud patterns in real customer traffic
• Strong analytical writing – briefs, reports, and intelligence summaries for mixed audiences
• Technical literacy- ability to read and understand technical write-ups of attack methods without needing to implement them
• Research discipline and critical thinking- structured methodology, source citation, and source credibility assessment

• Experience in Dark web research- access practices, navigation of marketplaces and forums- is a significant advantage
• Familiarity with fraud/forgery tools and the broader fraud ecosystem
• Background in intelligence, cybersecurity research, or financial crime

Description

Founded in 2002, AU10TIX is the global leader in AI driven identity verification and management, protecting the world’s largest brands against advanced fraud. The company’s future-proof product portfolio helps businesses provide frictionless customer onboarding and verification in 4-8 seconds while staying ahead of emerging threats and evolving regulatory requirements.

We are Looking for Head of Algorithms for:

• driving continuous improvement in detection rates across all algorithm domains (document fraud, deepfakes, biometrics) while ensuring production-grade performance, scalability, and reliability of deployed models
• Providing technical direction, mentorship, and career development to the Algo group
• Set the overall technical strategy and roadmap for all computer vision algorithm development
• Drive data labeling strategy and ownership across the organization, coordinate with QC, Product and various teams to define intake processes and SLAs
• Prepare and deliver technical presentations for diverse audiences: client-facing ML capability pitches, VP-level strategy decks, and internal architecture reviews
• Ensure PII compliance in algorithm pipelines and participate in cross-departmental compliance mapping initiatives

Model Development & Research

• Own and guide the design, training, and optimization of deep learning models for identity document classification, tampering detection, deepfake detection, and biometric analysis
• Lead model architecture decisions and drive migration to modern architectures

ML Lifecycle & Infrastructure

• Own the end-to-end ML pipeline: data gathering, labeling strategy, training, evaluation, versioning, and deployment
• Drive cloud migration of training pipelines to Cloud ML (compute clusters, experiment tracking, model registry, CI/CD integration)
• Oversee inference optimization: ONNX export, TensorRT FP16 acceleration, GPU benchmarking, and microservices packaging
• Define and maintain evaluation frameworks including demographic fairness testing, ROC/AUC analysis, FAR/FRR metrics, and detection rate tracking at fixed false-alarm thresholds

Requirements

• 10 years of hands-on experience in deep learning and computer vision, with at least 5 years in a senior leadership role managing team leads.
• Proven experience leading and scaling technical teams in a director-level or equivalent capacity
• Strong expertise in CNN architectures and computer vision pipelines
• Production experience with the full ML lifecycle: data collection, labeling, training, evaluation, optimization, and deployment
• Solid understanding of GPU inference optimization and benchmarking
• Strong communication skills, ability to present complex ML topics to both technical and non-technical audiences

Nice to Have

• Domain experience in identity verification, document analysis, or fraud detection
• Experience with deepfake detection (document-level and biometric)
• Experience with cloud ML platforms (Azure ML preferred: compute clusters, experiment tracking, model registry)
• Familiarity with unsupervised/semi-supervised methods
• Knowledge of microservices architecture patterns and containerized deployment (Docker, Kubernetes)
• Experience with object detection frameworks and segmentation models
• Background in LLM integration for document extraction tasks