Data Scientist

Job Description Duties and Responsibilities Improve models and algorithms to further optimize business outcomes. Work across the following areas: • Exploratory analysis: use data to suggest and prove hypotheses • Modeling: build optimization / predictive / statistical models to learn from data and estimate the unknowns - demand and sales forecasting, dynamic pricing for ancillary products, and demand planning • Data operations: query data, deploy models and automate pipelines in cloud • Set up sound time-based validation and honest baselines, and prove a model beats them before it ships. • Write clean, reviewable Python and SQL, merged through proper code review. • Help analyze live experiments and learn to spot a misleading readout. • Communicate findings clearly to technical and non-technical stakeholders. • Document work so a teammate can run and extend it without you. • Working with commercial teams to maximize the revenue by infusing AI & ML in their systems. Requirements and Qualifications: • BS in Physics, Mathematics, DataScience or Engineering discipline Up to 4 yrs relevant experience beyond first degree • Experience with common data science toolkits, programming languages (.py), visualisation tools and SQL/NoSQL databases. Machine and Deep Learning : • Experience building production ML systems, beyond notebooks and Kaggle competitions.· Solid understanding of machine learning algorithms, XGBoost, LightGBM, neural networks, decision trees, with a clear grasp of why you tuned what you tuned.· • Strong Python and hands-on experience with ML frameworks such as scikit-learn, TensorFlow, or PyTorch.· • Demonstrable understanding of forecasting and regression pitfalls - lag feature leakage, target leakage in cross-validation, high-cardinality categorical handling, and the trade-offs between MAE, MAPE, and RMSE.· • Ability to interpret models — SHAP, partial dependence, residual diagnostics — and explain results to non-technical stakeholders without dumbing them down.· • Hands-on Google Cloud Platform experience, particularly BigQuery (window functions, partitioning, cost-aware SQL) and Vertex AI (training jobs, model registry, endpoints, pipelines).· • Experience with propensity / take-up (purchase-probability) models and probability calibration is a plus.· • Exposure to time-series forecasting at scale (many related series), probabilistic forecasts, or demand that builds up toward a deadline is a plus.· • Nice-to-have: deep learning for tabular and time-series problems (TFT, N-BEATS, NeuralProphet, TabPFN, Chronos); AutoML tooling such as PyCaret for rapid baselining. Algorithm Engineering : • Strong ability to implement, improve, and deploy ML and mathematical models in Python (Golang a plus for performance-critical services).· • Experience productionizing models end-to-end, from SQL feature pipelines to deployed serving endpoints, on GCP using Vertex AI and BigQuery.· • Conduct systems tests for security, performance, and availability of deployed models.· • Develop and maintain design documentation, error analysis runbooks, and troubleshooting guides.· • Git-based workflows, CI/CD discipline, and code review hygiene.· • Monitoring discipline : drift detection, data quality checks, model performance tracking in production.· • Nice-to-have: experience with LLM-based or agentic tooling (LangGraph, MCP servers, prompt engineering for structured outputs, eval harnesses for LLM systems)