Data Scientist (On-Site in Boston, MA)

Genentech•Boston, MA

4d•Hybrid

About The Position

This role is based in the Innovation Accelerator (IA) team, the innovation engine and connective tissue for Design, Data and Data Science innovation strategy within Product Development Data Sciences (PDD). We translate our long-term PDD vision into actionable strategy, shaping and prioritizing innovative cross-functional use cases that span PDD, PD, and Pharma. As both integrators and incubators, we explore, prototype, and help productize solutions to deliver impact in close partnership with internal Roche teams and external collaborators. With a mindset rooted in openness, value creation, and adaptability, we navigate the innovation ecosystem to drive transformative impact and future readiness across the organization. This position is based in Boston, remotely for now, but will eventually be needed on site 3 days per week (approximately) . The Opportunity The IA Data Scientist plays a pivotal role in building and deploying AI/ML-powered digital solutions that transform how we develop medicines. You will partner closely with product managers, engineers, and UX researchers to design, test, and scale models that unlock actionable insights from clinical, operational, and real-world data. With a strong product-thinking mindset and deep technical fluency, you will help create intelligent tools that are scalable, ethical, and built for impact in regulated healthcare environments.. You support the development of statistical or ML models to address defined scientific and business questions. You conduct exploratory data analyses and feature engineering on clinical and operational datasets under supervision. You assist with simulation studies and benchmarking of analytical approaches. You contribute to the implementation of reproducible pipelines for preprocessing and model development using standard tooling. You work with engineering and science teams to support the deployment of simple models into user-facing applications. You monitor basic performance metrics and assist in documenting model specifications and limitations. You apply basic principles of responsible ML such as traceability and explainability in collaboration with more senior colleagues. You participate in agile development processes, including sprint planning and retrospective discussions. You actively contribute to internal team meetings, code reviews, and discussions around model design. You learn from mentorship, pair programming, and feedback while expanding expertise in applied data science for clinical innovation.

Requirements

You have a Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Applied Mathematics, Bioinformatics, or a related field.
You have a minimum of 2–4 years of hands-on experience applying data science or ML techniques to real-world problems in academic, internship, or industry settings; or an advanced degree with 0–2 years of equivalent work experience.
You are working proficient with Python or R, and have basic familiarity with ML libraries (e.g., scikit-learn, XGBoost).
You have an understanding of core statistical concepts, supervised learning, and experimental design.
You have exposure to basic software development practices including version control and testing.
You are familiar with one or more of the following areas: observational data (e.g., EHR), Bayesian methods, biomarker analysis, or knowledge-based ML.
You have attention to detail and quality work with an ability to manage and prioritize multiple projects simultaneously, including both long-term and short-term initiatives.
You have excellent collaboration skills, including statistical consulting skills, interpersonal skills to contribute effectively in cross-functional team settings, ability to influence others without authority, and ability to build strong collaborative relationships with scientific and non-scientific partners.
You have capacity for independent thinking and ability to make decisions based upon sound principles.
You exhibit excellent strategic agility including problem-solving and critical thinking skills, and agility that extends beyond technical domain.
You demonstrate respect for cultural differences when interacting with colleagues in the global workplace.
You possess excellent verbal and written communication skills, specifically in the areas of presentation and writing, with the ability to explain complex technical concepts in clear language.

Nice To Haves

Familiarity with ML development tools or platforms (e.g., Docker, MLflow, Airflow).
Experience working with structured or semi-structured healthcare datasets.
Awareness of regulatory or compliance frameworks such as GxP or HIPAA.
Exposure to cross-functional teams or agile workflows in academic or industry settings.

Responsibilities

You support the development of statistical or ML models to address defined scientific and business questions.
You conduct exploratory data analyses and feature engineering on clinical and operational datasets under supervision.
You assist with simulation studies and benchmarking of analytical approaches.
You contribute to the implementation of reproducible pipelines for preprocessing and model development using standard tooling.
You work with engineering and science teams to support the deployment of simple models into user-facing applications.
You monitor basic performance metrics and assist in documenting model specifications and limitations.
You apply basic principles of responsible ML such as traceability and explainability in collaboration with more senior colleagues.
You participate in agile development processes, including sprint planning and retrospective discussions.
You actively contribute to internal team meetings, code reviews, and discussions around model design.
You learn from mentorship, pair programming, and feedback while expanding expertise in applied data science for clinical innovation.