2026 Summer Intern - Statistician and Data Scientist

Roche•South San Francisco, CA

19h•$45 - $50•Onsite

About The Position

2026 Summer Intern - Statistician and Data Scientist Department Summary At Roche/Genentech, we thrive to deliver more benefits to our patients as part of our 10-year Pharma vision. An integral part of achieving this vision is to deliver new and innovative data analytics solutions to our scientists across Pharma Technical Operations (PT). PT Digital and Operational Excellence (PTE) is the organization that catalyzes the global development and execution of PT’s Digital and Operational Excellence strategy to enable PT to realize our performance promises. We build a strong cross-functional and inclusive community, put the power of data into the hands of our people, further develop the Lean and Digital skills across PT, and scale up our Digital and Advanced Analytics solutions, for the benefit of our colleagues and patients. We aim to activate data citizenship and digital mind, revolutionize our FAIR data ecosystem and systems landscape, re-design excellent processes, and generate transformative insights. We collaborate closely with global functions to deliver impactful value to our patients. This internship position is located in South San Francisco, on-site. The Opportunity Provide statistical expertise and develop data science solutions to Pharma technical operations, including process development, analytical method development, commercial manufacturing and quality control. Collaborate with cross-functional teams to identify key scientific and operational questions and define analytical approaches. Undertake research using scientific approaches and state-of-art methodologies, including machine learning and AI, to analyze complex datasets. Interpret results and communicate insights clearly, translating findings into actionable business and scientific recommendations. Develop software programs, algorithms, and automated processes to cleanse, integrate, and evaluate large datasets from multiple disparate sources. Program Highlights Intensive 12-weeks, full-time (40 hours per week) paid internship. Program start dates are in May/June (Summer). A stipend, based on location, will be provided to help alleviate costs associated with the internship. Ownership of challenging and impactful business-critical projects. Work with some of the most talented people in the biotechnology industry.

Requirements

Required Education: You meet one of the following criteria: Must be pursuing a Bachelor's Degree (enrolled student). Must have attained a Bachelor's Degree (not currently enrolled in a graduate program). Must be pursuing a Master's Degree (enrolled student). Must have attained a Master's Degree. Must be pursuing a PhD (enrolled student).
Required Majors: Statistics, Biostatistics, data science, and related fields.
Expertise and Experience: Solid experience in statistical modeling, machine learning, AI, uncertainty quantification, and applying statistical methods to pharmaceutical technical development and manufacturing.
Hands-on proficiency with statistical techniques (e.g., linear models) and tools such as R or Python.
Collaborative Skills: strong collaborative skills for working in multidisciplinary teams, and great communication skills for reporting and stakeholder liaison.
Problem-Solving and Project Management: Solid analytical and problem-solving skills, ability to manage multiple projects, and high attention to detail to ensure accuracy and reliability.
Continuous Improvement and Learning: Self-motivated, able to work independently, keen on staying updated with new developments in statistics and data science, and passionate about continuous improvement and learning.

Nice To Haves

Excellent communication, collaboration, and interpersonal skills.
Complements our culture and the standards that guide our daily behavior & decisions: Integrity, Courage, and Passion.

Responsibilities

Provide statistical expertise and develop data science solutions to Pharma technical operations, including process development, analytical method development, commercial manufacturing and quality control.
Collaborate with cross-functional teams to identify key scientific and operational questions and define analytical approaches.
Undertake research using scientific approaches and state-of-art methodologies, including machine learning and AI, to analyze complex datasets.
Interpret results and communicate insights clearly, translating findings into actionable business and scientific recommendations.
Develop software programs, algorithms, and automated processes to cleanse, integrate, and evaluate large datasets from multiple disparate sources.