Sr Associate Data Scientist
About this opportunity
Career Category
Quality
Job Description
ABOUT THE ROLE
The Global Quality Analytics and Innovation team leads the digital transformation and innovation effort throughout Amgen’s Quality organization. We are at the forefront of developing and rolling out data-centric digital tools, employing automation, artificial intelligence (AI), and generative AI to drive end-to-end quality transformation. We are seeking a highly motivated and experienced Data Scientist with a strong background in Generative AI, Large Language Models (LLMs), and MLOps, along with an understanding for Quality in regulated environments (e.g., GxP). This role will play a key part in designing, developing, and deploying scalable AI/ML solutions to drive innovation, efficiency, and regulatory compliance across the organization.
You will collaborate with cross-functional teams, including software engineers, data engineers, business stakeholders, and quality professionals to deliver AI-driven capabilities that support strategic business objectives. The ideal candidate is an analytical thinker with excellent technical depth, communication skills, and the ability to thrive in a fast-paced, agile environment.
Key Responsibilities
Design, build, and deploy generative AI and LLM-based applications using frameworks such as LangChain, LlamaIndex, and others.
Engineer reusable and effective prompts for LLMs like OpenAI GPT-4, Anthropic Claude, etc.
Develop and maintain evaluation metrics and frameworks for prompt engineering.
Conduct data quality assessments, data cleansing, and ingestion of unstructured documents into vector databases.
Build retrieval algorithms for relevant data identification to support LLMs and AI applications.
Ensure AI/ML development complies with GxP and other regulatory standards, fostering a strong Quality culture.
Partner with global and local teams to support regulatory inspection readiness and future technological capabilities in AI.
Share insights and findings with team members in an Agile (SAFe) environment.
Preferred Qualifications
Master’s degree and 2–4 years of experience in Software Engineering, Data Science, or ML Engineering
Experience in developing and deploying LLM applications.
Strong foundation in ML algorithms, data science workflows, and NLP.
Expertise in Python and ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
Familiarity with MLOps tools (e.g., MLflow, CI/CD, version control).
Experience with cloud platforms (AWS, Azure, GCP) and tools like Spark, Databricks.
Understanding of RESTful APIs and frameworks like FastAPI.
Experience with BI and visualization tools (e.g., Tableau, Streamlit, Dash).
Knowledge of GxP compliance and experience working in regulated environments.
Strong communication skills with the ability to explain complex topics to diverse audiences.
High degree of initiative, self-motivation, and ability to work in global teams.
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Job details
How this role compares
Computed from every other active Information Technology role in our database, not just this employer's listings.
We currently track 378 comparable Senior Information Technology roles across 34 biopharma companies.
Salary context
45 of 378 peers report a salary range (USD, annualized)
Peers share this role's job function and a matching or adjacent seniority level -- not necessarily the same therapeutic area or country.
Where these roles are based
Top locations among the 378 comparable roles
+ 15 more countries
Seniority mix
378 of 378 peers have a known seniority level
Therapeutic area mix
0 of 378 peers have a known therapeutic area; the rest are genuinely unlabeled, not hidden
No peers with a known therapeutic area yet.
Similar opportunities
The closest matches from our peer group, ranked by how similar they are, not how well you'd qualify for them -- treat this as market context, not a guaranteed shortlist; a weak match is labeled as one below.
How we calculate "similar"
No black box, no LLM guesswork: a deterministic score built from four normalized attributes. Here's this role's own peer group at different match levels, so you can see the mechanism, not just the result.
Every comparison starts from the same 100-point budget: 25 for working in the same function, 40 for the same therapeutic area, 20 for the same or adjacent seniority, 15 for the same country. A dimension we can't confirm on both sides contributes nothing, never a guess, never a free pass.
0 points, never a partial guess. A role we know almost nothing about beyond its function bottoms out at 25%; it never inflates to 100% just because there's little to compare against. Seniority uses a defined ladder (Associate → Manager → Associate Director → Senior → Principal → Director → Senior Director → Executive/VP) so "Director" and "Senior Director" count as adjacent, but "Director" and "Executive/VP" do not.