Financial Crime Research Scientist

We are looking for an experienced Financial Crime Research Scientist to develop realistic financial-crime scenarios and evaluation benchmarks for AI systems. The role involves modeling fraud, money laundering, and other cyber-enabled financial crimes, and building evaluation frameworks to measure how effectively AI models detect, explain, and refuse to assist illicit activity. The position is fully remote (based in Israel) and part of a team advancing AI safety and cybersecurity research.

Mission

Own the Financial Crime Research area and co-own Benchmarking. You will translate real-world fraud,money laundering and cyber-enabled financial-crime schemes into realistic scenario datasets and detection tasks, then build the benchmarks that measure how reliably models detect, explain and refuse to assist such activity.

Key responsibilities

• Model real financial-crime typologies (payment fraud, account takeover, laundering patterns, sanctions evasion) as structured, labelled scenario datasets.
• Design detection and refusal evaluations that measure a model's ability to flag and explain illicit activity without enabling it.
• Author scoring methodology, graders and inter-rater guidelines, and co-own the team's benchmarking standards.
• Partner with compliance and legal subject-matter experts to keep scenarios realistic, current and ethically sourced.
• Analyse results and communicate findings to both technical and policy audiences.

Required Skills & Experience (Must Have)

• M.A. / M.Sc. in Computer Science from an accredited university (degree completed; thesis track preferred).
• Prior hands-on experience at a cyber security company - a minimum of three years in a research, security engineering, threat-intelligence or fraud / financial-crime role at a company whose core business is cyber security.
• Direct experience in fraud detection, anti-money-laundering, financial-crime intelligence or payments security, ideally within a cyber security or fintech-security company.
• Strong data skills: Python, SQL and the ability to design, label and validate datasets.
• Understanding of statistical evaluation, class imbalance and detection metrics (precision, recall, ROC / PR curves).
• Clear written communication for mixed technical and regulatory audiences.

Advantageous

• Professional certifications such as CAMS, or equivalent applied experience.
• Familiarity with transaction-monitoring systems, graph analytics or entity resolution.
• Prior experience benchmarking machine-learning or LLM systems

What We Offer

• A frontline role shaping how the industry measures and governs AI cyber capability.
• A senior, collaborative research team and direct access to frontier models and tooling.
• Support for responsible publication of your research.

About us

With more than 2,000 customers and operations in 13 cities around the world, Innodata is the AI technology solutions provider-of-choice for 7 of the world’s biggest technology companies, as well as leading companies across financial services, insurance, law, and medicine. We’re at an exciting stage in the company and fortunate in these times to grow our team.

We’re an equal opportunity employer.

Only relevant applicants will be contacted