Data Scientist

Join Transak, a leading platform in the crypto and payments space, as a mid-level Data Scientist. In this role, you will work within the Risk & Fraud team to reduce fraud without adding friction for good users. You will own the work end to end, from the initial question to the decision leadership makes based on your analysis. Your work will have a clear impact on product, growth, and engineering, and will turn into product, policy, and revenue. You will also have the opportunity to work in a hybrid team environment, with a focus on personal and professional development, health and wellness benefits, and work/life balance.

• Conception, development, and implementation of machine learning models and deterministic rules to reduce fraud.
• Collaboration with cross-functional teams to analyze product funnels, design experiments, and identify conversion bottlenecks.
• Creation of dashboards and self-serve data products for various stakeholders, ensuring data quality and reliability.
• Strong SQL. You can navigate large, messy warehouses (BigQuery, Snowflake, Redshift, or similar) and write performant, readable queries
• 2 to 5 years of experience as a data scientist, analytics engineer, or quantitative analyst, ideally at a fintech, payments, marketplace, or consumer tech company
• Solid Python (or R) for analysis and modeling: pandas, scikit-learn, statsmodels, and at least one deep-learning or gradient-boosting framework (XGBoost, LightGBM, PyTorch, TensorFlow)
• Ownership. You treat ambiguous problems as opportunities and don't wait to be told what to analyze next
• Visualization and BI. Comfortable building dashboards in Looker, Metabase, Tableau, Superset, or similar
• Experimentation fluency. You understand the math behind A/B testing, sample sizing, power, and common pitfalls (peeking, multiple comparisons, novelty effects)
• Communication. You can explain a confusion matrix to a PM and a funnel drop-off to the CEO, in the same week, in the same tone
• Machine learning intuition. You can pick the right model for the problem, evaluate it honestly (precision/recall trade-offs, calibration, drift), and ship it responsibly
• Experience in crypto, payments, banking, fraud, or compliance
• Familiarity with dbt, Airflow, or similar data-stack tooling
• Exposure to causal inference (difference-in-differences, propensity scoring, uplift modeling)
• Experience deploying models to production (batch or real-time) alongside engineers
• Knowledge of AML / KYC frameworks or experience working with regulators