Data Science Engineer
On-sitePune, Maharashtra, India
Job Summary
Translate complex analytical findings into robust, repeatable artefacts embedded in the platform, partnering with domain experts across BFSI, manufacturing, and healthcare to solve high-value decision problems using statistical modelling, machine learning, and causal inference. Build end-to-end data science solutions from exploratory data analysis through production packaging using Python, pandas, and scikit-learn, while developing domain-adapted models for credit risk, fraud detection, predictive maintenance, and patient outcome modelling. Collaborate with data engineers to translate ad-hoc pipelines into governed, scheduled systems, create explainability artefacts for regulated environments, and drive structured A/B experiments to validate improvements. Mentor junior analysts and contribute to the Industry-Academia COE programme.
Required Qualifications
- 3+ years in a data science or analytical engineering role delivering models to production in enterprise environments
- Expert-level Python for data analysis: pandas, NumPy, scipy, statsmodels, and scikit-learn
- Confident with SQL across large analytical datasets
- Strong grounding in statistical inference, experimental design, and the ability to distinguish signal from noise in messy enterprise data
- Experience with at least one domain-specific modelling area: fraud/risk scoring, demand forecasting, churn prediction, anomaly detection, or survival analysis
- Familiarity with ML experiment tracking (MLflow or equivalent) and a structured approach to documenting model assumptions and limitations
- Bachelor's or Master's degree in Statistics, Mathematics, Economics, Computer Science, or a related quantitative field
Desired Qualifications
- Experience applying causal inference methods (DiD, IV, propensity score matching) to evaluate business interventions in enterprise settings
- Exposure to time-series forecasting at enterprise scale using Prophet, NeuralProphet, or deep learning architectures (N-BEATS, TFT)
- Familiarity with Bayesian modelling frameworks (PyMC, Stan) for uncertainty quantification in regulated decision contexts
- Published case studies or conference presentations on applied data science in BFSI, manufacturing, or healthcare
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