Senior/Lead Product Analyst [Antifraud]

We are building the next generation of our Antifraud & Disputes system and are looking for a Product Analytics Senior/Lead to own the analytical, ML, and decision-making foundation behind how we detect, prevent, and manage fraud and disputes across products.

You will shape how we prioritise antifraud initiatives, build and scale ML models for fraud and dispute detection, and design the business logic behind risk decisions, ensuring we protect the business and customers while minimising friction and cost. This is a high-impact opportunity to define an end-to-end antifraud analytics function from the ground up, partnering closely with Product, Engineering, Risk, and Operations teams to turn ideas into scalable production systems.

What you'll do:

• Own Antifraud & Disputes Analytics end-to-end — metrics, dashboards, monitoring, data quality, and analytical support for business decisions

• Define the team's roadmap and priorities, allocating resources to the highest-impact problems

• Build and own ML models for Antifraud & Disputes across the full lifecycle — from feature engineering to deployment and monitoring

• Manage antifraud-related costs (vendors, tooling, operations) and drive efficiency without compromising risk or customer experience

• Partner with Product and Engineering to shape solutions and influence priorities for fraud prevention and disputes

• Optimise Fraud Operations at scale, working with a 150+ person team through data, automation, and process redesign

• Build self-service analytics and LLM-based tools so other teams can answer common questions independently

• Define and track the framework for measuring antifraud impact — losses, approval/conversion rates, disputes, costs, and friction

• Turn complex fraud and dispute problems into structured investigations and solutions across rules, ML, product, and operations

• Evaluate and manage external vendors — testing, benchmarking, and driving adoption decisions

What makes you a great fit:

• A mathematical or technical degree from a top-tier university (MSU, MIPT, HSE, MSTU, MEPhI, NSE, etc.)

• Expert knowledge of mathematical statistics, probability theory, and systematic thinking

• Hands-on experience building and deploying ML models in production (feature engineering, validation, monitoring)

• Advanced SQL and Python

• Hands-on experience with Tableau, Superset, or similar BI tools to translate data into actionable insights

• Ability to understand business objectives and transfer them into technical analytical tasks