Interested in this Data Scientist role at Gilead Sciences?
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About This Role
At Gilead, we’re creating a healthier world for all people. For more than 35 years, we’ve tackled diseases such as HIV, viral hepatitis, COVID\-19 and cancer – working relentlessly to develop therapies that help improve lives and to ensure access to these therapies across the globe. We continue to fight against the world’s biggest health challenges, and our mission requires collaboration, determination and a relentless drive to make a difference.
Every member of Gilead’s team plays a critical role in the discovery and development of life\-changing scientific innovations. Our employees are our greatest asset as we work to achieve our bold ambitions, and we’re looking for the next wave of passionate and ambitious people ready to make a direct impact.
We believe every employee deserves a great leader. People Leaders are the cornerstone to the employee experience at Gilead and Kite. As a people leader now or in the future, you are the key driver in evolving our culture and creating an environment where every employee feels included, developed and empowered to fulfil their aspirations. Join Gilead and help create possible, together.
Job Description
Responsibilities:
AI Operations, Contractor Delivery \& Hands\-On Technical Work
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- Works as part of a team responsible for managing contractors, technical delivery partners, and AI workstreams across applied AI initiatives.
- Supports contractor onboarding, work planning, technical direction, delivery coordination, quality review, and accountability for assigned work.
- Provides hands\-on technical direction for AI prototypes, model development, application patterns, data pipelines, and production AI systems.
- Reviews technical designs, architecture decisions, model evaluation plans, code quality, implementation tradeoffs, and production\-readiness.
- Contributes to prototypes, proof\-of\-concepts, notebooks, design documents, technical spikes, and code reviews when needed.
- Promotes scientific rigor, reproducibility, engineering excellence, responsible AI practices, documentation, and maintainable delivery patterns.
AI Research, Applied Innovation \& Product Value
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- Leads development, evaluation, deployment, and scaling of AI capabilities supporting research, development, clinical, regulatory, safety, and enterprise use cases.
- Applies product thinking to ensure AI solutions address clear user needs, workflow realities, business priorities, adoption goals, and measurable outcomes.
- Partners with Product Management \& Experiences to understand user needs, prioritize opportunities, define success metrics, and support adoption.
- Uses experimentation, user feedback, benchmarking, and iterative delivery to validate assumptions and improve AI capabilities over time.
- Identifies opportunities to use emerging AI technologies to accelerate scientific discovery and operational effectiveness.
- Uses experimentation, user feedback, benchmarking, and iterative delivery to validate assumptions and improve AI capabilities over time.
- Identifies opportunities to use emerging AI technologies to accelerate scientific discovery and operational effectiveness.
Technical Architecture \& Engineering Excellence
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- Designs and guides AI solution architectures for assigned projects and business domains.
- Guides development of Retrieval\-Augmented Generation systems, agentic workflows, prompt and context engineering patterns, evaluation harnesses, model monitoring, and Langfuse\-based observability.
- Sets expectations for production\-quality code, automated testing, version control, reproducible experiments, scalable deployment patterns, and operational documentation.
- Develops reusable AI frameworks, tools, accelerators, platforms, and services that enable faster delivery across Research, Development, and enterprise functions.
- Helps troubleshoot complex issues across data quality, model behavior, latency, reliability, security, scalability, cost, compliance, and user experience.
- Responsible AI, Governance \& Production Operations
- Ensures AI solutions follow applicable governance, privacy, security, regulatory, and responsible AI expectations.
- Implements practical approaches for Large Language Model evaluation, groundedness assessment, hallucination risk management, traceability, and quality measurement.
- Uses platforms such as Langfuse or equivalent approved tooling for LLM tracing, debugging, prompt and response analysis, observability, evaluation workflows, and production monitoring.
- Supports Machine Learning Operations, Large Language Model Operations, continuous integration and delivery, model monitoring, observability, and operational support practices.
Collaboration \& Stakeholder Engagement
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- Collaborates with Product Management \& Experiences, Business Delivery Excellence, Enterprise AI \& Governance Excellence, ARC translational AI teams, and Development partners.
- Partners with scientists, therapeutic area leaders, clinical teams, regulatory functions, Information Technology, Privacy, and Drug Development Systems to prioritize high\-impact AI opportunities.
- Communicates technical concepts, product strategy, risks, tradeoffs, and delivery progress clearly to technical and non\-technical audiences.
- Contributes to ARC initiatives that advance AI capabilities, governance, adoption, product innovation, and operational excellence.
Requirements
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Minimum Education \& Experience
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- PhD in Computer Science, Artificial Intelligence, Machine Learning, Computational Biology, Statistics, Engineering, or related discipline with 4\+ years of relevant industry experience.
- MS in a related discipline with 8\+ years of relevant experience.
- BS in a related discipline with 10\+ years of relevant experience.
- Demonstrated expertise in Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, Generative AI, or related disciplines.
- Experience guiding technical contributors, contractors, or cross\-functional project teams, including technical direction, coaching, delivery oversight, and quality review.
- Hands\-on experience building, evaluating, and deploying AI or machine learning solutions in applied research, product, or enterprise environments.
Core Technical Requirements
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- Strong hands\-on programming skills in Python and practical experience with modern AI and machine learning libraries such as PyTorch, TensorFlow, or equivalent approved technologies.
- Experience in clinical trial operational data, real\-world data, and supporting trial feasibility, site selection and forecasting.
- Experience building Generative AI applications with LangChain, LangGraph, Semantic Kernel, Microsoft Agent Framework, Langfuse, AWS\-native AI services, Microsoft Azure services where appropriate, or equivalent approved enterprise technologies.
- Experience designing and implementing Retrieval\-Augmented Generation systems, including chunking, embeddings, vector search, reranking, grounding, citation patterns, retrieval evaluation, and response quality measurement.
- Experience developing agentic AI workflows, tool\-use patterns, orchestration approaches, guardrails, human\-in\-the\-loop review models, prompt engineering, context engineering, and model evaluation techniques.
- Experience with APIs, microservices, notebooks, Git\-based development, automated tests, containerization, deployment patterns, monitoring, and operational support for AI systems.
- Experience deploying AI solutions in regulated environments with appropriate governance, security, privacy, compliance, scalability, reliability, and responsible use controls.
AI Domain Expertise
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- Strong knowledge of Generative AI, Large Language Models, advanced analytics, and applied machine learning.
- Experience in one or more of the following areas: foundation models, multimodal AI, agentic AI systems, scientific machine learning, knowledge graphs, Retrieval\-Augmented Generation, Natural Language Processing, or advanced deep learning.
- Experience designing evaluation frameworks for Large Language Models, including accuracy, groundedness, hallucination risk, robustness, latency, cost, safety, user acceptance, and Langfuse\-based tracing or evaluation workflows.
- Experience translating research and experimental AI concepts into scalable production capabilities.
Cloud, Engineering \& Operations Stack
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- Extensive experience with Machine Learning Operations, Large Language Model Operations, continuous integration and delivery, cloud\-based AI infrastructure, observability, model monitoring, and automated testing.
- Experience working with Amazon Web Services and Microsoft Azure, including AI, machine learning, data, security, and scalable compute services.
- Experience using Langfuse or equivalent approved tooling for LLM application tracing, debugging, prompt and response analysis, production observability, evaluation workflows, and quality measurement.
- Experience applying software engineering methodologies, scalable AI architectures, reusable components, documentation standards, and maintainable production systems.
Product, Leadership \& Business Acumen
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- Demonstrated product mindset with experience translating technical capabilities into solutions that deliver measurable user and business value.
- Experience partnering with product managers, designers, engineers, scientists, and business stakeholders throughout the product lifecycle.
- Proven ability to align technical strategies with business goals and communicate complex concepts effectively to non\-technical stakeholders.
- Demonstrated success working with multidisciplinary teams, managing contractors or technical delivery partners, and mentoring technical contributors.
- Ability to balance experimentation and innovation with execution, adoption, operational impact, and measurable outcomes.
- Proven ability to influence programs, projects, and initiatives in a matrixed environment.
Preferred Qualifications
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- Experience applying AI in life sciences, drug development, clinical research, healthcare, or regulated industries.
- Experience contributing to publications, patents, open\-source projects, technical communities, or internal technical standards.
- Strong analytical, communication, organizational, and stakeholder management skills.
Ability to travel as needed.
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The salary range for this position is: $210,375\.00 \- $272,250\.00\. Gilead considers a variety of factors when determining base compensation, including experience, qualifications, and geographic location. These considerations mean actual compensation will vary. This position may also be eligible for a discretionary annual bonus, discretionary stock\-based long\-term incentives (eligibility may vary based on role), paid time off, and a benefits package. Benefits include company\-sponsored medical, dental, vision, and life insurance plans\*.
For additional benefits information, visit:
https://www.gilead.com/careers/compensation\-benefits\-and\-wellbeing
\* Eligible employees may participate in benefit plans, subject to the terms and conditions of the applicable plans.
For jobs in the United States:
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Gilead Sciences Inc. is committed to providing equal employment opportunities to all employees and applicants for employment, and is dedicated to fostering an inclusive work environment comprised of diverse perspectives, backgrounds, and experiences. Employment decisions regarding recruitment and selection will be made without discrimination based on race, color, religion, national origin, sex , age, sexual orientation, physical or mental disability, genetic information or characteristic, gender identity and expression, veteran status, or other non\-job related characteristics or other prohibited grounds specified in applicable federal, state and local laws. In order to ensure reasonable accommodation for individuals protected by Section 503 of the Rehabilitation Act of 1973, the Vietnam Era Veterans' Readjustment Act of 1974, and Title I of the Americans with Disabilities Act of 1990, applicants who require accommodation in the job application process may contact [email protected] for assistance.
For more information about equal employment opportunity protections, please view the 'Know Your Rights' poster.
NOTICE: EMPLOYEE POLYGRAPH PROTECTION ACT
YOUR RIGHTS UNDER THE FAMILY AND MEDICAL LEAVE ACT
Gilead Sciences will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, (c) consistent with the legal duty to furnish information; or (d) otherwise protected by law.
Our environment respects individual differences and recognizes each employee as an integral member of our company. Our workforce reflects these values and celebrates the individuals who make up our growing team.
Gilead provides a work environment free of harassment and prohibited conduct. We promote and support individual differences and diversity of thoughts and opinion.
For Current Gilead Employees and Contractors:
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Please apply via the Internal Career Opportunities portal in Workday.
Salary Context
This $210K-$272K range is above the 75th percentile for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Gilead Sciences, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($241K) sits 25% above the category median. Disclosed range: $210K to $272K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Gilead Sciences AI Hiring
Gilead Sciences has 5 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Foster City, CA, US, Raleigh, NC, US. Compensation range: $189K - $272K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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