PAID INTERNSHIP : TO BE DISCUSSED / FINALIZED ONCE SELECTED. Role & responsibilities Help design and test ML models for real business / data problems Work on data cleaning, feature creation and basic model training in Python Run experiments, compare algorithms and document results Support the team in converting notebooks into simple scripts / APIs Prepare small demos, slides or reports to explain your work Preferred candidate profile Final-year student or recent graduate in CS / IT / Data Science / related field Good basics in Python, statistics and ML algorithms (regression, classification, etc.) Some project or course experience in applied ML (Kaggle, college projects, mini apps) Comfortable working with Jupyter, Git and common ML libraries (pandas, scikit-learn, etc.) Curious, willing to learn fast, and able to commit part-time for 6 months.
Key Role Highlights: Build LLM-powered and agentic AI features as Python services integrated into data products, collaborating with data scientists and solution architects on production-ready GenAI solutions. Required Candidate profile Early-career AI engineer with 1–3 years’ experience in Python, LLMs and agentic AI, building and integrating GenAI services, and eager to learn, experiment and own solutions in a startup set-up
Role & Responsibilities Develop, validate and deploy machine learning models on structured data for retail, FMCG and fintech use-cases (e.g. churn, cross-sell, credit / risk, pricing). Design end-to-end data pipelines : data cleaning, feature engineering, model training, evaluation and monitoring. Analyse large transactional and customer datasets to derive actionable insights for marketing, sales, supply chain and risk teams. Work closely with domain experts to translate business questions into analytical problems and present findings in a clear, decision-ready format. Document experiments, maintain reproducible code (Python, SQL, Git) and contribute to model governance and best practices . Preferred Candidate Profile Submitted PhD thesis (or in final stages) in Statistics, Mathematics, Computer Science, Econometrics, Operations Research or related quantitative field; OR have upto 1 - 3 years of relevant ML experience on structured data. Strong grounding in ML algorithms for structured data (logistic/linear regression, tree-based models, gradient boosting, regularisation, model diagnostics). Hands-on experience with Python (pandas, scikit-learn) and SQL , comfortable working with large, messy real-world datasets. Prior exposure or strong interest in retail/FMCG analytics or fintech / credit risk / customer analytics . Looking to transition from academia to industry or grow early industry career, with clear communication, curiosity, and a collaborative mindset.