Data Science Intern - Pharma & Biotech

Weatherden — Data Science & Strategic Insights 
Team: Data Science & Strategy   Duration: 12-week internship or 12-month industrial placement (flexible to fit your programme)  Location: London, hybrid  Start date: Flexible — to be agreed with successful candidate 
About Weatherden  Weatherden is a physician, scientist and led clinical development partner supporting biotech, pharma, and investors to develop medicines in the right direction. We focus on reducing technical, strategic, and execution risk across the full development pathway – from translational strategy and IND readiness through to First-in-Human, clinical proof-of-concept, and pivotal studies.     With over 200 projects delivered, more than 500 clinical trials supported, and experience across numerous therapeutic areas and modalities, our team combines deep clinical, regulatory, statistical and operational expertise. We are London headquartered and composed predominantly of MD/PhD-qualified team members and senior industry leaders with decades of cumulative industry experience. 
About the Role  We are looking for a candidate who can combine practical data science skills with knowledge of the pharmaceutical and biotechnology sector. The successful candidate will help turn complex scientific, clinical and industry data into structured information and actionable insights that support strategic decision-making. 
Working with data science, engineers, clinical development and strategy colleagues, the candidate will contribute to projects that bring together information from multiple sources, improve its quality and accessibility, and use analytical methods to identify patterns, emerging trends and potential white-space opportunities – areas where unmet needs or gaps in existing approaches may warrant further investigation. 
The project includes helping to build a structured, searchable intelligence database, developing repeatable data collection and scalable workflows, and interpreting the data to support clinical decision making. The balance between data engineering, analysis and research will be shaped around the candidate’s strengths, development interests and business priorities, rather than a single predefined workstream.
Key Responsibilities 

• Translate scientific and business questions into clear analytical tasks, working with colleagues to define useful datasets, measures and outputs. 
• Collect, clean, combine and validate data from relevant scientific, clinical and industry sources, documenting provenance, assumptions and limitations. 
• Help develop and maintain structured intelligence datasets or a searchable database, using consistent definitions and quality checks. 
• Apply exploratory data analysis, statistical methods and visualisation to identify trends, relationships and changes across the pharma/biotech landscape. 
• Contribute to data pipelines, API connectors or automated refresh processes that reduce manual effort and keep information current (experience-dependent).  
• Present findings clearly to technical and non-technical colleagues through concise summaries, visualisations or dashboards. 
• Document code and methods so that outputs are reproducible, maintainable and useful to others. 
• Combine analytical findings with sector knowledge to highlight potential unmet needs, competitive gaps and white-space opportunities for further assessment, distinguishing genuine signals from gaps in data coverage. 

What We’re Looking For 
Essential 

• Currently studying towards, or recently completed, a relevant degree in data science, bioinformatics, computational biology/chemistry or similar, biotechnology, computer science, statistics, or a related discipline; alternatively, a life sciences background with demonstrable quantitative or programming skills. 
• Practical programming skills in Python or R, demonstrated through coursework, research, personal projects or relevant experience. 
• A sound understanding of data science fundamentals, including data preparation, exploratory analysis, basic statistics and visualisation. 
• Knowledge of the pharma/biotech sector and an understanding of the drug development process, gained through study, research or industry experience. 
• A careful, critical approach to working with incomplete or inconsistent data, including recognising uncertainty, bias and limitations. 
• Clear written and verbal communication, with the ability to explain analytical findings and their relevance to scientific or business questions. 
• Curiosity, structured problem-solving and a willingness to learn new tools and work collaboratively across disciplines. 

Desirable  We welcome complementary strengths and will shape the project scope around the successful candidate’s experience and development interests. 

• SQL and relational database experience, including structuring, querying or combining datasets. 
• Experience using APIs or building API connectors to retrieve and integrate data. 
• Familiarity with data pipelines, ETL processes or workflow automation. 
• Experience with scientific, clinical trial, regulatory or competitive intelligence datasets. 
• Dashboard development or interactive data visualisation. 
• Exposure to machine learning, natural language processing or text mining to organise and analyse unstructured information. 
• Familiarity with version control, testing and reproducible analytical workflows. 
  

What You’ll Gain 

• Practical experience applying data science to real questions in pharma and biotech. 
• The opportunity to contribute to reusable datasets, analytical tools or an intelligence database used by scientific and strategy colleagues. 
• Experience identifying trends and assessing potential opportunities while accounting for evidence quality and uncertainty. 
• Collaboration with colleagues across data science, clinical development and strategy. 
• A defined project matched to your strengths, with opportunities to develop skills in data engineering, analytics or scientific intelligence. 

How to Apply  To apply, please send a CV and a short covering note (max one page) outlining relevant data science experience, pharma/biotech knowledge and areas you would like to develop.
If available, include a code sample, project link or brief example of an analytical project, explaining your contribution