Data Scientist

About the Role 

The FCC DnA team is seeking a motivated Data Scientist to join our AI Modeling team and help build the next generation of AI-powered products and capabilities. 

This role is ideal for an early-career Data Scientist with a strong foundation in Statistics, Mathematics, Machine Learning and programming who enjoys solving real-world business problems using data. 

You will work alongside experienced Data Scientists and partner with Product, Engineering and Business teams to explore data, build and evaluate models, and contribute to AI solutions across Machine Learning, NLP and Generative AI. 

We are looking for someone who is curious, analytical, hands-on and eager to develop strong modeling skills while contributing to production-oriented AI solutions. 

Key Responsibilities 

• Perform exploratory data analysis to understand data patterns, relationships, distributions and data-quality issues. 

• Develop baseline statistical and Machine Learning models for classification, regression, clustering and other predictive use cases. 

• Perform data preparation, feature engineering and feature selection. 

• Compare multiple modeling approaches using appropriate performance metrics and summarize the trade-offs. 

• Apply techniques such as train/test splitting and cross-validation to evaluate model performance. 

• Conduct error analysis and help identify opportunities to improve model accuracy and reliability. 

• Document modeling assumptions, methodology, results and limitations clearly. 

• Collaborate with senior Data Scientists to validate and refine modeling approaches. 

• Develop clean, reusable Python code and contribute to shared modeling components. 

• Support integration of models into AI applications and production workflows. 

• Contribute to NLP, Generative AI, RAG and other Applied AI initiatives based on project needs. 

• Participate in knowledge-sharing sessions and continuously build expertise across Data Science and AI. 

Required Qualifications 

Experience 

• Approximately 1-2 years of experience in Data Science, Machine Learning, Statistical Modeling, Analytics or related areas. 

• Candidates with strong academic, research, internship or project experience in statistical modeling and Machine Learning may also be considered. 

• Exposure to solving real-world data or modeling problems is preferred. 

Statistical & Modeling Skills 

• Strong foundation in Statistics, Probability and Mathematics. 

• Understanding of hypothesis testing, probability distributions, sampling and statistical inference. 

• Knowledge of regression and classification techniques. 

• Familiarity with decision trees, random forests, boosting algorithms, clustering and other common Machine Learning approaches. 

• Understanding of model performance metrics such as precision, recall, F1, ROC-AUC, RMSE and related measures. 

• Ability to compare models and explain performance differences. 

• Basic understanding of overfitting, underfitting and bias-variance trade-offs. 

Technical Skills 

• Strong Python fundamentals and experience with libraries such as pandas, NumPy, scikit-learn and related Data Science tools. 

• Working knowledge of SQL. 

• Experience with data visualization and exploratory analysis. 

• Familiarity with Git and collaborative development practices. 

• Exposure to cloud platforms, APIs, Docker or MLOps concepts is a plus. 

• Exposure to NLP, Large Language Models, embeddings, RAG or Generative AI is desirable but not mandatory. 

We Are Looking for Individuals Who 

• Demonstrate curiosity and a strong desire to understand why a model works, not simply how to run an algorithm. 

• Are comfortable experimenting with different modeling approaches and learning from results. 

• Pay attention to data quality, model assumptions and analytical rigor. 

• Take ownership of assigned work and proactively ask questions when requirements are unclear. 

• Communicate technical findings clearly to both technical and non-technical audiences. 

• Enjoy working collaboratively across Data Science, Product and Engineering teams. 

• Continuously learn new statistical, Machine Learning and AI techniques. 

Education 

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

Our Interview Practices

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.