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

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 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.

We Are Looking for Individuals Who

·  Ask which modeling approach is most appropriate for the problem and can explain why.

·  Challenge assumptions rather than automatically selecting the most complex algorithm.

·  Are comfortable working with ambiguous business problems and defining a structured analytical approach.

·  Balance experimentation with analytical rigor and production considerations.

·  Take ownership of modeling workstreams and independently drive them to completion.

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

·  Mentor junior team members and raise the quality of modeling practices around them.

·  Collaborate effectively with Product, Engineering and business stakeholders.

·  Remain current with developments across statistical modeling, Machine Learning and broader Applied AI.

Education

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

To maintain a fair and genuine hiring process, we kindly ask that all candidates participate in interviews without the assistance of AI tools or external prompts. Our interview process is designed to assess your individual skills, experiences, and communication style. We value authenticity and want to ensure we’re getting to know you—not a digital assistant. To help maintain this integrity, we ask to remove virtual backgrounds and include in-person interviews in our hiring process. Please note that use of AI-generated responses or third-party support during interviews will be grounds for disqualification from the recruitment process.

Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process.