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This listing doesn't post a salary. Most similar roles pay
$157,512–$214,600.
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
37 open roles
on FindRole.
Listed pay typically runs
$168,980–$218,680
across 28 roles with salary data.
The Sr Manager, Data Scientist, Technical Development leads applied analytics, statistical modeling, ML/AI, and automation use cases to advance scientific insight within the Technical Development team. This role involves partnering with scientists and IT teams to frame scientific questions, prototype analytical approaches, and develop reusable, workflow-integrated decision-support capabilities. Key responsibilities include building predictive models, LLM/GenAI-enabled knowledge management systems, image-based modeling, and stability analytics. The successful candidate will utilize Python, R, and knowledge of ontologies, knowledge graphs, and scalable data pipelines to solve complex technical development problems. The work focuses on translating operational questions into analytical strategies, ensuring data governance, and moving exploratory work toward scalable products. This position requires expertise in scientific workflow automation, process-data modeling, and creating robust, interpretable outputs for high-stakes scientific decision-making.
Identify and prioritize high-value data science, AI, and analytics opportunities across Technical Development.
Partner with scientists to prototype, test, and iterate analytical approaches within real development workflows.
Translate scientific and operational questions into clear analytical problem statements with defined success measures.
Lead cross-functional initiatives to align stakeholders on analytical strategies, data standards, and platform capabilities.
Design and advance scientific knowledge management systems, including ontologies, knowledge graphs, and GenAI solutions.
Define and implement evaluation frameworks to ensure the robustness, explainability, and trusted use of AI solutions.
Build and promote the reuse of analytical assets like ML models, standardized datasets, and automation scripts.
Transition exploratory analytics into scalable, supportable products in collaboration with IT and Digital Solutions teams.
What we're looking for
BA/BS with 10+ years of experience; MA/MS with 8+ years; or PhD with 2+ years in data science, statistics, engineering, or a related quantitative field.
Multiple years of applied data science experience including statistical modeling, machine learning, AI, automation, data engineering, visualization, or knowledge management.
Strong programming skills in Python, R, or similar languages to develop reproducible analytical workflows and reusable code.
Experience translating scientific or operational questions into analytical strategies, data requirements, modeling approaches, and interpretable outputs.
Working knowledge of data quality, metadata, ontologies, knowledge graphs, data governance, and scalable data pipelines.
Experience with methods such as stability modeling, image-based analytics, clone ranking, or scientific workflow automation.
Effective communication skills to align scientific, digital, and technology stakeholders on analytical strategies and adoption approaches.
Experience partnering with IT or engineering teams in cloud, LLMOps, or data lake environments (preferred).
Experience working in GMP-adjacent or regulated environments with a focus on validation and quality expectations (preferred).
Experience coaching or guiding less experienced data scientists or project team members on analytical methods and responsible AI use (preferred).
Python
R
SQL
TensorFlow
PyTorch
scikit-learn
AWS
Azure
GCP
Tableau
Power BI
DataOps
CI/CD
Generative AI
Machine Learning
Big Data
Agile
Scrum
Data Governance
Data Visualization
Python
LLM
generative-AI
RAG
MLOps
AWS
Data Engineering
Predictive Analytics
Data Modeling
Prompt Engineering
Data Pipelines
Knowledge Base Engineering
Responsible AI
Model Governance