Summer Internship: Data Insights AI Summer Intern

BeiGene•Cambridge, MA

About The Position

BeOne continues to grow at a rapid pace with challenging and exciting opportunities for experienced professionals. When considering candidates, we look for scientific and business professionals who are highly motivated, collaborative, and most importantly, share our passionate interest in fighting cancer. Internship program dates: June 8 - August 21, 2026 Education Required: Education: Master’s or PhD preferred in Statistics, Mathematics, Computer Science, Data Science, Bioinformatics, Industrial Engineering, or a related field. The Data Insights (DI) team is seeking a highly motivated Summer Intern to work on real, high-impact business initiatives spanning data insights, digital product/platform development, and applied AI delivery. You will partner closely with Product, Data Engineering, and Statistics teams - translating business problems into analytical approaches, evaluating solution options, and supporting delivery with reusable analytics assets to enable sustained iteration and scale. This role offers a unique opportunity to apply cutting-edge AI to real-world challenges in biotechnology and pharmaceuticals - helping accelerate drug development and improve patient outcomes.

Requirements

Programming: Proficiency in Python or R for data wrangling and analysis (e.g., pandas/tidyverse, visualization, basic statistical analysis). Ability to read, understand, and improve existing code is a plus.
Data & SQL: Solid SQL fundamentals (joins, aggregations). Bonus for window functions, query performance awareness, and working with large datasets.
Analytical Thinking: Strong problem-structuring skills—able to translate ambiguous business questions into a hypothesis → analysis → insights → recommendations narrative. High bar for data quality, rigor, and interpretability.
Communication & Ownership: Clear, concise written communication with the ability to produce business-ready deliverables.

Nice To Haves

Hands-on experience using LLMs/AI tools (e.g., ChatGPT, Copilot, Claude) for requirements clarification, problem decomposition, drafting/refining SQL and analysis code, and automating summaries/documentation. Demonstrates strong prompt iteration, result evaluation, and traceable outputs.
Familiarity with or practical exposure to one or more of: RAG/knowledge retrieval, text extraction and classification, automated report generation, agent/workflow orchestration, or offline evaluation methods.
Experience with enterprise data platforms: Databricks preferred; familiarity with Spark and efficient EDA on large-scale datasets.
Applied experience in professional statistical environments: RStudio Workbench (Posit) or similar, with exposure to collaborative server/cloud development (version control and job scheduling).

Responsibilities

Data Insights & Analytics Delivery: Translate business questions into clear analytical approaches; perform data extraction, cleaning, EDA, and synthesis of actionable insights. Conduct core analyses such as trend/cohort segmentation, benchmarking, anomaly detection, and data quality validation. Deliver structured outputs (key findings, supporting evidence, recommendations, and risks) and present in reviews/stakeholder discussions.
AI & Digital Product / Platform Development Support end-to-end work on AI, data products, and digital platforms: requirements shaping, solution research, rapid prototyping/POCs, AI-evals, and impact measurements. Help embed LLM capabilities into workflows - e.g., automated meeting notes and action items, text-to-structure extraction, knowledge retrieval (RAG) - assisted analysis, and report generation. Contribute to scalable evaluation frameworks (metrics, test sets, acceptance criteria) to enable reuse and broader adoption.
Cross-Functional Collaboration & Execution Partner with Data Engineering, Statistics, Medical, and Clinical teams to track data readiness, ensure definition alignment, and maintain delivery timelines. Maintain project artifacts (task lists, decisions, dependencies) to improve traceability, clarity, and execution velocity.