Key Responsibilities Data Science & Modeling • Understand business problems and convert them into ML problem statements • Perform EDA, feature engineering, and feature selection • Build and evaluate models using: • Regression, classification, clustering • Time-series forecasting • Anomaly detection and recommendation systems • Apply model evaluation techniques (cross-validation, bias-variance tradeoff, metrics selection) ML Engineering & Deployment • Productionize ML models using Python-based pipelines • Build reusable training and inference pipelines • Implement model versioning, experiment tracking, and retraining workflows • Deploy models using APIs or batch pipelines • Monitor model performance, data drift, and prediction stability Data Engineering Collaboration • Work with structured and semi-structured data from multiple sources • Collaborate with data engineers to: • Define data schemas • Build feature pipelines • Ensure data quality and reliability Stakeholder Communication • Present insights, model results, and trade-offs to non-technical stakeholders • Document assumptions, methodologies, and limitations clearly • Support business decision-making with interpretable outputs Required Skills Core Technical Skills • Programming: Python (NumPy, Pandas, Scikit-learn) • ML Libraries: XGBoost, LightGBM, TensorFlow / PyTorch (working knowledge) • SQL: Strong querying and data manipulation skills • Statistics: Probability, hypothesis testing, distributions • Modeling: Supervised & unsupervised ML, time-series basics ML Engineering Skills • Experience with model deployment (REST APIs, batch jobs) • Familiarity with Docker and CI/CD for ML workflows • Experience with ML lifecycle management (experiments, versioning, monitoring) • Understanding of data leakage, drift, and retraining strategies Cloud & Tools (Any One Stack is Fine) • AWS / GCP / Azure (S3, BigQuery, SageMaker, Vertex AI, etc.) • Workflow tools: Airflow, Prefect, or similar • Experiment tracking: MLflow, Weights & Biases (preferred) Good to Have • Experience in domains like manufacturing, supply chain, fintech, retail, or consumer tech • Exposure to recommendation systems, forecasting, or optimization • Knowledge of feature stores and real-time inference systems • Experience working with large-scale or noisy real-world datasets Educational Qualification • Bachelor’s or master’s degree in computer science, Statistics, Mathematics, Engineering, or related fields