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About This Role
### The Position
Why Genentech
We're passionate about delivering on Our Promise to improve the lives of patients and create healthier communities for all. We foster a culture of inclusivity, integrity and creativity while boldly pursuing answers to the world's most complex health challenges and transforming society.
Genentech's Data, Digital, and Analytics (DDA) team is dedicated to solving complex healthcare challenges and improving patient outcomes. DDA empowers business partners across Commercial, Medical, and Government Affairs (CMG) to make impactful decisions by leveraging data, analytics, and AI/ML to enable fast, targeted actions in rapidly evolving business contexts.
DDA fosters a unified understanding of customers, actions, and outcomes by transforming the business insight supply chain from the traditional reactive service model to a modern proactive product model, which integrates analytics and insights seamlessly into CMG's evolving digital, data, and automation platforms, creating scalable solutions and eliminating silos. In DDA, you will work as a trusted, objective advisor and expert, recommending critical decisions and actions to be taken with credibility and a focus on driving measurable impact. You will be part of a diverse, inclusive team that reflects the world we serve, thriving in a welcoming culture built on collaboration and innovation.
The Opportunity
The Principal Data Scientist in Data, Analytics, and AI serves as a visionary technical leader and strategic thought partner, driving the innovation, development, and industrialization of next\-generation enterprise AI/ML applications. This pivotal role requires deep expertise in building enterprise recommendation engines alongside advanced predictive modeling and forecasting solutions tailored specifically for the Commercial, Marketing, and Medical Affairs domains. Operating with a high degree of independence, the Principal Data Scientist translates ambiguous business challenges into rigorous analytical roadmaps, elevates team thinking, and provides the strategic technical insights necessary to shape organizational priorities. This position requires a rare blend of deep technical mastery—spanning applied statistics, deep learning, and forecasting—and the executive presence required to influence cross\-functional strategy while maintaining compliance with Genentech’s standards.
- Technical Leadership \& Architecture: Lead and guide the data science lifecycle for high\-priority AI/ML projects, serving as the subject matter expert for advanced analytical methodologies across DDA.
- Product \& Capability Ownership: Drive the end\-to\-end development, scaling, and deployment of AI solutions to optimize marketing efficiency, medical affairs strategy, and customer experience.
- Strategic Partnership: Act as a critical technical advisor to leadership and cross\-functional stakeholders; leverage deep technical expertise and proactive research to influence long\-term commercial and clinical business strategies.
- Advanced Methodology Application: Apply ML, deep learning, and applied statistics to extract high\-value insights from complex, disparate structured and unstructured data sources.
- Cross\-Functional Synergy:Collaborate seamlessly with Data Science Product Owners, ML Engineers, and other teams to build production\-grade, scalable applications. Openly share perspectives to elevate the team’s technical maturity and optimally weigh technical vs. business trade\-offs.
- Influence \& Storytelling: Translate highly complex algorithmic architectures and predictive insights into concise, compelling business narratives that inspire trust and drive immediate operational action among non\-technical stakeholders.
Who You Are
- Bachelor’s Degree and 7\+ years of hands\-on experience in AI with a proven track record of technical leadership, innovation, and independent project execution.
- 3\+ years of dedicated experience applying Recommender Systems (e.g., content\-based/collaborative filtering) and predictive/forecasting models to enterprise\-scale data to drive tangible business results.
- Deep proficiency in quantitative fields including Deep Learning (applied to recommendation engines or advanced forecasting), Causal Inference, and advanced statistical modeling.
- Strong experience working with large\-scale, complex data environments utilizing big data platforms (Hadoop, Spark) and cloud\-computing ecosystems (AWS).
- Advanced proficiency in Python and deep familiarity with open\-source machine learning and agentic libraries/frameworks (e.g., LangChain, CrewAI, TensorFlow, PyTorch) to solve enterprise\-level problems.
Preferred
- Master’s or PhD in Data Science, Computer Science, Applied Mathematics, Statistics or a related quantitative field
- Deep knowledge of highly regulated environments (healthcare, pharma, biotech) with specific experience utilizing Commercial, Medical Affairs, claims, marketing, and longitudinal patient data.
- A proven ability to successfully pivot and apply transferable artificial intelligence and machine learning techniques across diverse business domains and ambiguous use cases.
- Experience using industry\-standard tools like Tableau, Qlik, or Data Studio to translate complex data into accessible, democratized insights.
- Strong communication, leadership, and time management skills, backed by a track record of open\-source contributions, peer\-reviewed publications, or patents.
This position is based in South San Francisco, CA at our Genentech Campus and offers a hybrid schedule working 3 days per week on campus
Relocation benefits are not available for this posting.
The expected salary range for this position based on the primary location of South San Francisco, CA is $207,480 and $385,320\. Actual pay will be determined based on experience, qualifications, geographic location, and other job\-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below.
Benefits
Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.
If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.
Salary Context
This $207K-$385K range is above the 75th percentile for Data Scientist roles in our dataset (median: $162K across 211 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 3,824 AI roles we're tracking, Data Scientist positions make up 7% of the market. At Genentech, 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 $200,000 based on 697 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($296K) sits 48% above the category median. Disclosed range: $207K to $385K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Genentech AI Hiring
Genentech has 3 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $207K - $385K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 1,990 tracked positions. That's 26% above the national 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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