Data Scientist – AI Modelling (3 -5 years only)
Wolters Kluwer (company site, opens in a new tab)3–5 yrsPuneOn-siteDirect career pageEst. 12–22 LPA
Posted 13h ago
Reach people at Wolters Kluwer
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Wolters Kluwer you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Own end-to-end development of statistical and Machine Learning models, including design, evaluation, validation, and deployment collaboration with Engineering and Product teams.
- Translate ambiguous business problems into structured modeling solutions across Statistical Modeling, Machine Learning, NLP and Generative AI, ensuring clear communication of model choices, assumptions, and limitations.
- Lead post-deployment model monitoring, performance tracking, and mentor junior Data Scientists while building reusable modeling frameworks.
Minimum Requirements
- 3-5 years of hands-on experience in Data Science, Machine Learning, Statistical Modeling, or Applied AI.
- Bachelor's or Master's degree in a quantitative discipline such as Statistics, Mathematics, Data Science, Computer Science, Artificial Intelligence, Engineering, or Economics.
- Strong proficiency in Python, SQL, model development lifecycle experience, and collaboration with cross-functional teams.
- Candidates may be required to appear onsite during recruitment and participate in interviews without AI assistance as per company policy.
Ideal Candidate Profile
- Experienced in independently driving modeling workstreams from data exploration through production deployment with strong analytical and validation rigor.
- Strong strategic thinker who balances experimentation with practical production considerations, emphasizing model explainability and appropriate metric selection.
- Comfortable collaborating broadly with Product, Engineering and business stakeholders while mentoring junior members and contributing to advanced AI initiatives including NLP and Generative AI.