Data Science Internship

About the role

As a Data Science Intern, you will analyze complex data sets, develop practical data mining and statistical analysis skills, and contribute to machine learning and AI-enabled product solutions. You will work closely with data scientists and engineers to explore model performance, data quality, retrieval quality, AI output quality, and customer-impact metrics for products that use trusted legal and professional content.

Key responsibilities

• Contribute to collecting, cleaning, organizing, and documenting datasets for analysis, model development, and evaluation.
• Collaborate with data science, engineering, product, and UX team members to develop, test, and improve predictive models, machine learning workflows, and AI-enabled product capabilities.
• Conduct exploratory data analysis to identify trends, patterns, data-quality issues, and opportunities to improve customer outcomes.
• Support experiments involving generative AI, large language models, embeddings, retrieval-augmented generation, prompt patterns, and agentic workflows.
• Contribute to model and AI-output evaluation, including metrics for accuracy, grounding, relevance, reliability, bias, safety, and customer usefulness.
• Create visualizations, reports, dashboards, or notebooks that communicate insights, experiment results, and model performance clearly.
• Document assumptions, limitations, evaluation methods, and responsible AI considerations so work can be reviewed, reproduced, and reused by global teams.

Required profile

• Currently enrolled student pursuing a degree in data science, computer science, statistics, mathematics, engineering, or a related field.
• Experience using programming languages and tools such as Python, R, SQL, notebooks, and common data science libraries.
• Foundation in statistics, machine learning, data visualization, and analytical problem‑solving.
• AI fluency or curiosity, including awareness of generative AI, large language models, embeddings, RAG, model evaluation, model limitations, and responsible AI practices.
• Effective problem‑solving, critical‑thinking, verbal and written communication skills.
• Detail‑oriented and able to work both independently and collaboratively.
• Interest in learning, experimentation, data‑driven decision‑making, and staying current with AI and data science trends.

Required skills

• Python
• R
• SQL
• Jupyter notebooks
• Data science libraries (e.g., pandas, scikit‑learn)