This role pays more than
65%
of similar roles. Most pay
$159,093–$219,650
— the shaded band above.
At the midpoint, this role pays about
$209k
versus about
$189k
for comparable roles.
Based on 240 similar postings.
Employer
About Gilead Sciences
Gilead Sciences, Inc. is a leading American biopharmaceutical company specializing in discovering, developing, and commercializing innovative medicines for unmet medical needs.
Gilead Sciences currently has
22 open roles
on FindRole.
Listed pay typically runs
$169,320–$219,120
across 21 roles with salary data.
Sr Manager, Data Scientist, Technical Development leads applied analytics, statistical modeling, machine learning, AI, and automation initiatives to advance scientific insight within the Technical Development team. This role involves partnering with scientists and technical stakeholders to translate complex scientific questions into analytical problem statements, prototyping validated approaches, and developing reusable, workflow-integrated decision-support capabilities. Key projects include predictive modeling, LLM/GenAI-enabled knowledge management, image-based analytics, stability modeling, and process-data modeling. The successful candidate will utilize Python or R to build reproducible workflows and manage data assets like ontologies and knowledge graphs. Essential skills include expertise in data engineering, visualization, and high-level communication to align cross-functional teams on technical strategies. This role solves critical problems in scientific discovery by moving exploratory analytics toward scalable products within a regulated environment focused on drug development and manufacturing processes.
Identify and prioritize high-value data science, AI, and analytics opportunities across Technical Development.
Translate scientific and operational questions into clear analytical problem statements and technical requirements.
Prototype, test, and iterate on analytical approaches to support real development workflows.
Develop advanced knowledge management capabilities including ontologies, knowledge graphs, and GenAI-enabled discovery tools.
Establish evaluation frameworks for AI solutions to ensure performance, robustness, and explainability.
Build and promote the reuse of shared analytical assets like models, datasets, and automation scripts.
Collaborate with IT and platform teams to transition exploratory analytics into scalable, production-ready products.
Contribute to enterprise-wide LLM and GenAI initiatives while ensuring compliance with data governance standards.
What we're looking for
A BA/BS degree with 10+ years of experience, an MA/MS with 8+ years, or a PhD with 2+ years in a relevant quantitative field.
Multiple years of applied data science experience including statistical modeling, machine learning, AI, automation, and data engineering.
Strong programming skills in Python, R, or similar languages to develop reproducible workflows and reusable code.
Experience translating scientific or operational questions into analytical strategies, data requirements, and interpretable outputs for decision-making.
Working knowledge of data quality, metadata, ontologies, knowledge graphs, and scalable data pipelines.
Experience with technical development areas such as stability modeling, image-based analytics, or scientific workflow automation.
Effective communication skills to align scientific, digital, and technology stakeholders on analytical strategies and adoption approaches.
Preferred experience in GMP-adjacent/regulated environments and coaching less experienced team members on analytical methods.