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About This Role
United States - Remote JOB ID: R-257589 LOCATION: United States - Remote WORK LOCATION TYPE: Remote DATE POSTED: Oct. 13, 2026 CATEGORY: Information Systems SALARY RANGE: 103,852.15USD -140,505.85 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. Amgen is advancing a broad and deep pipeline and portfolio of medicines to treat heart disease, obesity and obesity-related conditions, rare diseases, inflammatory conditions and cancer. 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.
Data Scientist
What you will do
Let’s do this. Let’s change the world. In this vital role you will serve as a core data science expert and AI-enabled capabilities within Amgen's Global Supply Chain Data & Analytics team. The role will focus primarily on designing, developing, evaluating, deploying, and continuously improving AI assistants, agentic AI capabilities, and advanced analytics solutions supporting the analytics ecosystem. This individual will partner with product owners, business stakeholders, data engineers, software engineers, architects, and delivery partners to translate complex supply chain problems into secure, scalable, and reliable solutions that improve user productivity and business decision-making. The ideal candidate combines hands-on data science and GenAI expertise, business understanding, stakeholder communication, and experience operationalizing solutions in an enterprise environment.
Roles & Responsibilities:
- Serve as the data science technical lead and subject matter expert for assigned AI-enabled capabilities across analytics capabilities
- Partner with product owners and supply chain stakeholders to translate business problems into prioritized AI and analytics use cases, technical approaches, success measures, and delivery plans
- Design, develop, evaluate, and enhance GenAI and agentic AI solutions using large language models, retrieval-augmented generation, semantic search, prompt engineering, and multi-agent workflows
- Establish evaluation and monitoring approaches for AI solutions, including accuracy, relevance, robustness, safety, user feedback, and appropriate human review
- Apply machine learning, statistical modeling, and time-series analysis to supply chain use cases involving demand, supply, inventory, capacity, cost, risk, and operational performance
- Collaborate with data engineers to develop trusted analytical datasets, reusable features, data quality checks, lineage, and documentation supporting AI and analytics products
- Work with software engineering, architecture, platform, testing, and delivery partners to integrate data science capabilities into enterprise applications and support testing, deployment, production performance, and issue resolution
- Provide technical guidance and reviews to data scientists, engineers, and delivery partners; coordinate dependencies and promote reusable approaches across connected products
- Develop technical documentation, operating procedure and contribute to technical reviews
- Communicate findings, solution tradeoffs, risks, recommendations, and business impact to technical and non-technical stakeholders while contributing through the Agile delivery model and applicable AI and data governance processes
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 Data Scientist with these qualifications.
Basic Qualifications:
- Master's degree and 1 to 3 years of Computer Science, IT or related field experience OR
- Bachelor's degree in data science, business, statistics, data mining, applied mathematics, business analytics, engineering, computer science, or a related field, with 2 years of relevant experience. OR
- Associate's degree in data science, business, statistics, data mining, applied mathematics, business analytics, engineering, computer science, or a related field, with 6 years of relevant experience
- Hands-on experience with Python and SQL for data analysis, feature engineering, machine learning, natural language processing, and AI solution development
- Experience designing or developing GenAI and large-language-model capabilities such as AI assistants, retrieval-augmented generation, embeddings, semantic search, prompt engineering, or agentic workflows
- Strong foundation in machine learning, statistical analysis, exploratory data analysis, time-series methods, and model evaluation
- Experience progressing data science or AI solutions from experimentation into production, including testing, version control, monitoring, and MLOps or LLMOps practices
- Ability to work with large, complex datasets and collaborate on data modeling, data quality, lineage, and reusable analytical data pipelines
- Demonstrated ability to translate business problems into technical approaches, independently lead a technical workstream, and communicate results clearly
Preferred Qualifications:
- Experience with supply chain, manufacturing, or biotech analytics use cases such as demand and supply planning, inventory, capacity, cost, financial planning, or operational KPIs
- Experience with cloud-based data and AI platforms, large-scale data processing, and integration of AI capabilities into enterprise applications
- Understanding of responsible AI, AI governance, security, compliance, observability, and ongoing solution performance management
- Experience supporting analytical or reporting products and working with cross-functional teams and delivery partners in an Agile or SAFe environment
Professional Certifications:
- Data Science, Artificial Intelligence, or Machine Learning Certification (Preferred)
- Cloud or Data Platform Certification (Optional)
Soft Skills:
- Excellent critical-thinking, analytical, and problem-solving skills
- Strong communication, collaboration, and stakeholder-management skills
- Demonstrated ability to provide technical leadership and influence without formal authority
- Ability to connect technical work to business outcomes and explain complex topics clearly
- Strong presentation, knowledge-sharing, and mentoring skills
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 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. We make all employment decisions without regard to race, color, religion, sex or any other characteristic protected by applicable law.
We provide reasonable accommodations to applicants and employees with disabilities, consistent with legal requirements. Please contact us to request accommodation.
Salary Context
This $103K-$140K range is in the lower quartile for Data Scientist roles in our dataset (median: $161K across 138 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 9,079 AI roles we're tracking, Data Scientist positions make up 4% 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 1,503 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($122K) sits 36% below the category median. Disclosed range: $103K to $140K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Amgen AI Hiring
Amgen has 7 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Remote, US, Los Angeles, CA, US. Compensation range: $140K - $269K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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