Data Scientist
Core
Translate complex datasets into actionable business insights using statistical analysis and machine learning, delivering production-ready models.
Role type
Senior IC data scientist (classical ML & Bayesian modeling)
Builds
Production ML models for business-critical use cases (classification, regression, ranking, anomaly detection, time-series forecasting)
Domain
Fintech / Emerging markets / High-social-impact industry
Deliverable
production ML models
Required skills
Classical ML algorithms (gradient boosting, random forests, SVMs, logistic regression, clustering, dimensionality reduction), Probabilistic and Bayesian modeling, Python (pandas, NumPy, SciPy, MLflow), SQL (complex queries, window functions), Model evaluation frameworks (cross-validation, calibration, AUC, RMSE), Experiment design and A/B testing, Cloud data warehouses (AWS Redshift, BigQuery, Snowflake)
Preferred skills
Survival modeling, Causal inference, Marketing mix modeling (MMM), Time-series forecasting libraries (Prophet, statsmodels, sktime), MLOps pipelines, Model deployment on AWS (SageMaker, Lambda, ECS)
Technologies
PyMC, PyMC-Marketing, scikit-learn, XGBoost, LightGBM, CatBoost, MLflow, W&B, AWS SageMaker, AWS Lambda, AWS ECS
Responsibilities
Design, build, and evaluate classical machine learning models; Apply probabilistic and Bayesian modeling techniques; Perform EDA, feature engineering, and data wrangling; Collaborate with data engineers to validate data pipelines; Develop and track model performance metrics; Translate business questions into statistical problems; Maintain clean, reproducible, and well-documented code
Seniority
Mid-Senior (3–4 years experience)
