# Data Scientist – AI Modelling (3 -5 years only) at Wolters Kluwer

Wolters Kluwer is hiring a Data Scientist – AI Modelling (3 -5 years only) in Pune, India. Level rates it Builds AI ●●●●; you can [apply on Wolters Kluwer](https://jobsbylevel.com/go/bd95a2af-c2c6-457b-b745-97759a047102?utm_medium=ai-agent).

AI Level 4, AI centrality 90 out of 100. IND-Pune-Smartworks.

## Details

- Company: [Wolters Kluwer](https://jobsbylevel.com/companies/wolters-kluwer)
- AI level: AI Level 4 (score 90 out of 100)
- Location: IND-Pune-Smartworks
- Posted: October 28, 2026
- Apply: https://jobsbylevel.com/go/bd95a2af-c2c6-457b-b745-97759a047102?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=data-scientist-ai-modelling-3-5-years-only-at-wolters-kluwer-b024ab

## Prepare for this job

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## Description

About the Role 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. Key Responsibilities · 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. Required Qualifications 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. Statistical & Modeling Skills · 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. Technical Skills · Strong Python and modern Data Science ecosystem experience. · Strong SQL and data manipulation skills. · Experience developing production-quality, reusable modeling

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