The FCC DnA team is seeking an experienced Data Scientist to join our AI Modeling team and develop scalable Machine Learning, statistical and AI solutions.
This role is ideal for a hands-on Data Scientist who can independently translate business problems into modeling problems, evaluate multiple analytical approaches and recommend solutions based on data, statistical evidence and business requirements.
You will work across Statistical Modeling, Machine Learning, NLP and Generative AI while partnering with Product, Engineering and Business teams to take solutions from experimentation toward production.
We are looking for someone who goes beyond building a model and can clearly explain why a particular modeling approach was selected, how it was validated and what its limitations are.

Independently design, develop and evaluate statistical and Machine Learning models.

- Perform exploratory analysis, feature engineering, feature selection and data-quality assessment.
- Apply cross-validation, hyperparameter tuning, out-of-sample testing and other robust validation techniques.
- Conduct detailed error analysis, sensitivity analysis and model diagnostics.
- Identify and address issues such as data leakage, class imbalance, multicollinearity, sampling bias and overfitting.
- Evaluate model explainability, stability and performance trade-offs.
- Support model monitoring, performance tracking and drift analysis after deployment.
- Contribute to NLP, Generative AI, RAG and hybrid AI solutions where appropriate.
- Mentor junior Data Scientists and conduct code and model reviews.
- Experience
- Approximately 3-5 years of hands-on experience in Data Science, Machine Learning, Statistical Modeling or Applied AI.
- Demonstrated experience independently developing and validating predictive models.




- Experience working across the model development lifecycle from data exploration through validation and productionization.
- Experience working with cross-functional Product, Engineering and Business teams.

Strong understanding of statistical inference, probability, hypothesis testing and experimental design.

- Strong hands-on experience with regression, classification, tree-based models, ensemble methods and other Machine Learning techniques.
- Experience with algorithms such as logistic/linear regression, random forest, gradient boosting, XGBoost/LightGBM or comparable techniques.
- Experience designing benchmark, baseline and challenger models.
- Understanding of bias-variance trade-offs, sampling bias, class imbalance, data leakage and model stability.
- Experience with explainability techniques such as feature importance, SHAP or similar approaches.
- Ability to assess trade-offs between predictive performance, interpretability, complexity and maintainability.

Strong Python and up-to-date Data Science ecosystem experience.

- Strong SQL and data manipulation skills.
- Experience developing production-quality, reusable modeling code.
- Familiarity with APIs and frameworks such as FastAPI.
- Familiarity with Git, Docker, CI/CD, cloud platforms such as Azure/AWS and MLOps practices.
- Experience with model deployment, monitoring and versioning is preferred.
- Experience with NLP, Generative AI, LLMs, embeddings, RAG or Agentic AI is desirable and will enable contribution across broader AI initiatives.

Clearly articulate model performance, assumptions, limitations and business implications.

- Mentor junior team members and raise the quality of modeling practices around them.
- Remain current with developments across statistical modeling, Machine Learning and broader Applied AI.

Bachelor's or Master's degree in Statistics, Mathematics, Data Science, Computer Science, Artificial Intelligence, Engineering, Economics or another quantitative discipline.

📌 Data Scientist Ml Ai Python (Pune)
🏢 Wolters Kluwer India Private
📍 Pune