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About This Role
US - California - Thousand Oaks JOB ID: R-256841 LOCATION: US - California - Thousand Oaks WORK LOCATION TYPE: Remote DATE POSTED: Sep. 29, 2026 CATEGORY: Operations SALARY RANGE: 160,896.50USD -217,683.50 USD Join Amgen’s Mission of Serving Patients
At Amgen, if you feel like you’re part of something bigger, it’s because you are. Our shared mission—to serve patients living with serious illnesses—drives all that we do.
Since 1980, we’ve helped pioneer the world of biotech in our fight against the world’s toughest diseases. With our focus on four therapeutic areas –Oncology, Inflammation, General Medicine, and Rare Disease– we reach millions of patients each year. Amgen is advancing a broad and deep pipeline of medicines to treat cancer, heart disease, inflammatory conditions, rare diseases, and obesity and obesity-related conditions. As a member of the Amgen team, you’ll help make a lasting impact on the lives of patients as we research, manufacture, and deliver innovative medicines to help people live longer, fuller happier lives.
Our award-winning culture is collaborative, innovative, and science based. If you have a passion for challenges and the opportunities that lay within them, you’ll thrive as part of the Amgen team. Join us and transform the lives of patients while transforming your career.
Principal Data Scientist
What you will do
Let’s do this. Let’s change the world. In this vital role you will play a key part in designing, developing, and deploying scalable AI/ML solutions and agentic systems to drive innovation, efficiency, and regulatory compliance across the organization.
You will collaborate with multi-functional teams, including software engineers, data engineers, business partners, and quality professionals to design and implement responsible and ethical AI-driven capabilities that support strategic business objectives.
- Innovation & Research Translation: Stay at the forefront of AI/ML advancements—especially in the agentic AI and knowledge graph spaces—and translate emerging techniques into enterprise-ready applications.
- Agentic System Design & Innovation: Architect and deploy agent-based and multi-agent AI/ML systems to enhance automation, decision-making, and operational efficiency, leveraging modern frameworks and GenAI capabilities.
- Data Excellence & MLOps Governance: Champion robust data science practices and develop scalable, auditable MLOps pipelines. Enforce standards for versioning, testing, monitoring, documentation, and security to ensure high-quality model lifecycle management.
- Enterprise Integration & Cross-Functional Alignment: Partner with technology, product, and business team members to align AI initiatives with enterprise goals. Ensure solutions are traceable, interoperable, and embedded within Amgen’s digital Quality ecosystem.
- Responsible & Ethical AI: Embed principles of ethical AI—including fairness, transparency, privacy, and explain-ability—into the design and deployment of AI/ML solutions. Ensure alignment with Amgen’s Quality and Compliance standards and global regulatory frameworks.
What we expect of you
We are all different, yet we all use our unique contributions to serve patients. The professional we seek is a go getter with these qualifications.
Basic Qualifications:
Doctorate degree and 2 years of Data Science, Software/ML Engineering, experience
OR
Master’s degree and 4 years of Data Science, Software/ML Engineering, experience
OR
Bachelor’s degree and 6 years of Data Science, Software/ML Engineering, experience
OR
Associate’s degree and 10 years of Data Science, Software/ML Engineering, experience
OR
High school diploma / GED and 12 years of Data Science, Software/ML Engineering, experience
Preferred Qualifications:
- Demonstrated success providing technical leadership to teams delivering AI, machine learning, generative AI, analytics, or intelligent automation products in production.
- Experience delivering AI-enabled solutions within GxP-regulated biotechnology, pharmaceutical, healthcare, or life-sciences environments.
- Strong understanding of GxP principles, data integrity, computer-system validation, risk-based validation, change control, and regulated model lifecycle management.
- Proven expertise designing and implementing AI, machine learning, and generative AI applications, including agent-based or multi-agent systems.
- Experience with frameworks and patterns such as LangChain, LangGraph, LlamaIndex, retrieval-augmented generation, GraphRAG, vector search, and knowledge graphs.
- Advanced proficiency in Python and common machine-learning libraries such as PyTorch, TensorFlow, scikit-learn, or equivalent technologies.
- Experience with large language models, prompt and context engineering, tool calling, model evaluation, guardrails, and human-in-the-loop design.
- Proficiency with data-engineering technologies and methods, including SQL, Apache Spark, data pipelines, data modeling, and distributed processing.
- Experience developing full-stack applications and integrating AI solutions with enterprise platforms, APIs, data products, and business workflows.
- Strong knowledge of MLOps and LLMOps practices and tools, including MLflow, CI/CD pipelines, automated testing, observability, model monitoring, and infrastructure as code.· Experience with cloud and data platforms such as Databricks, AWS, Microsoft Azure, or Google Cloud Platform.
- Demonstrated ability to manage a portfolio of AI opportunities, balance experimentation with production delivery, and connect technical outcomes to measurable business value.
- Experience developing reusable AI capabilities, technical standards, enablement materials, or communities of practice.· Strong problem-solving skills and the ability to operate effectively in complex, ambiguous, and rapidly evolving environments.
- Excellent written and verbal communication skills, with the ability to influence technical, business, quality, and executive stakeholders.
- Demonstrated ability to mentor others, lead through influence, and build productive relationships across functions and geographies.
- High degree of initiative, accountability, self-motivation, and effectiveness in global, cross-functional teams.
What you can expect of us
As we work to develop treatments that take care of others, we also work to care for your professional and personal growth and well-being. From our competitive benefits to our collaborative culture, we’ll support your journey every step of the way.
The expected annual salary range for this role in the U.S. (excluding Puerto Rico) is posted. Actual salary will vary based on several factors including but not limited to, relevant skills, experience, and qualifications.
In addition to the base salary, Amgen offers a Total Rewards Plan, based on eligibility, comprising of health and welfare plans for staff and eligible dependents, financial plans with opportunities to save towards retirement or other goals, work/life balance, and career development opportunities that may include:
A comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions, group medical, dental and vision coverage, life and disability insurance, and flexible spending accounts
A discretionary annual bonus program, or for field sales representatives, a sales-based incentive plan
Stock-based long-term incentives
Award-winning time-off plans
Flexible work models where possible. Refer to the Work Location Type in the job posting to see if this applies.
and make a lasting impact with the Amgen team.
careers.amgen.com
In any materials you submit, you may redact or remove age-identifying information such as age, date of birth, or dates of school attendance or graduation. You will not be penalized for redacting or removing this information.
Application deadline
Amgen does not have an application deadline for this position; we will continue accepting applications until we receive a sufficient number or select a candidate for the position.
Sponsorship
Sponsorship for this role is not guaranteed.
As an organization dedicated to improving the quality of life for people around the world, Amgen fosters an inclusive environment of diverse, ethical, committed and highly accomplished people who respect each other and live the Amgen values to continue advancing science to serve patients. Together, we compete in the fight against serious disease.
Amgen is an Equal Opportunity employer and will consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or any other basis protected by applicable law.
We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
Salary Context
This $160K-$217K range is above the median for Data Scientist roles in our dataset (median: $167K across 172 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 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At Amgen, 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 $190,000 based on 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. Disclosed range: $160K to $217K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Amgen AI Hiring
Amgen has 8 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Los Angeles, CA, US, Thousand Oaks, CA, US, Remote, US. Compensation range: $171K - $253K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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