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)