Data Scientist : Machine Learning

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.