Data Scientist – AI Modelling (3 -5 years only)

Overview

Hands-on Data Scientist on the FCC DnA AI Modeling team, developing scalable Machine Learning, statistical and AI solutions and taking them from experimentation toward production.

What you'll do

• Translate business problems and requirements into appropriate analytical and modeling approaches.
• Independently design, develop and evaluate statistical and Machine Learning models.
• Establish appropriate baseline and challenger models and objectively compare alternative approaches.
• Perform exploratory analysis, feature engineering, feature selection and data-quality assessment.
• Define appropriate model evaluation metrics based on the business problem and the cost of different types of errors.
• 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.
• Clearly document model methodology, assumptions, limitations and validation results.
• Partner with Engineering and MLOps teams to transition models from experimentation into production.
• Support model monitoring, performance tracking and drift analysis after deployment.
• Build reusable modeling components and frameworks that can be leveraged across multiple use cases.
• Contribute to NLP, Generative AI, RAG and hybrid AI solutions where appropriate.
• Mentor junior Data Scientists and conduct code and model reviews.
• Communicate modeling recommendations and trade-offs effectively to business, Product and technical stakeholders.

What you'll need

• 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.
• Strong understanding of feature engineering, feature selection and model diagnostics.
• Experience designing benchmark, baseline and challenger models.
• Strong understanding of model evaluation and the ability to select metrics appropriate to the business problem.
• 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 modern 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.
• Bachelor's or Master's degree in Statistics, Mathematics, Data Science, Computer Science, Artificial Intelligence, Engineering, Economics or another quantitative discipline.

Nice to have

• Experience with model deployment, monitoring and versioning is preferred.
• Experience with NLP, Generative AI, LLMs, embeddings, RAG or Agentic AI is desirable.

Details

• Location: Pune, India (Smartworks).
• Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process.
• Interviews must be completed without AI tools or external prompts; virtual backgrounds must be removed and in-person interviews are part of the process.