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