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Location
In office (Hyderabad)
Seniority
Senior · 5+ years exp
Employment
Full-Time

Confirmed on the employer's own hiring board on Nov 3, 2026. First seen by Alion on Nov 3, 2026. Amgen scores A on the Alion truth index.

Overview
Company
Impact
Profile match
Amgen is an American biotechnology company founded in Thousand Oaks in 1980 that became one of the first firms to turn recombinant protein research into large commercial medicines. Its history rests on Epogen and Neupogen, and its current portfolio spans bone health with Prolia and Evenity, inflammation with Otezla and Tezspire, cardiovascular disease with Repatha, oncology including the bispecific antibody Blincyto, and a biosimilars business built on its own manufacturing capacity. The company is listed on Nasdaq, is a component of the Dow Jones Industrial Average, and expanded further into rare disease through its acquisition of Horizon Therapeutics.

Career Category

Clinical

Job Description

Data Science Engineer

GCF Level 4

As a Data Science Engineer, you will develop and test assigned data pipelines, AI integrations and analytical tools with guidance from experienced colleagues. You will translate defined user and Data Science needs into documented technical components using established engineering practices. You will build pharmaceutical, clinical-data and AI-workflow knowledge while using approved tools, reviewed code and appropriate data-access controls.

Responsibilities

  • Work with data scientists to understand user needs and technical requirements.
  • Implement assigned API, retrieval or ML components with guidance.
  • Prepare permitted datasets and record sources and versions for assigned analytics and AI workflows.
  • Develop and test pipeline components and approved AI integrations using Python, SQL and version control.
  • Run specified regression and failure-case tests, investigate issues and document results.
  • Review AI-assisted code and record reproducible results.
  • Collaborate across regions with Data Science, Clinical Development, IS and data-platform partners.
  • Support acceptance testing and document AI limitations and required human review.
  • Support delivered products, identify improvements and prepare technical documentation, handoffs and training materials.

Accountabilities

  • Deliver well-tested, documented components that meet assigned technical requirements.
  • Follow data-access and privacy rules, protect secrets and escalate unsafe AI behavior or data exposure.
  • Contribute reusable components and improvements to data pipelines and analytical tools.
  • Take ownership of agreed deliverables and raise issues affecting quality or timelines.
  • Maintain code quality, maintainability and reliability.
  • Deliver tested components and support documentation on time.
  • Recommend implementation options and make code, testing and documentation decisions within agreed standards.

Basic Qualifications

One of the following:

  • Master’s degree; OR
  • Bachelor’s degree and 2 years of related experience.

Preferred Qualifications

  • 5 to 9 years of experience in Data Engineer
  • Master’s degree in Computer Science, Data Science, Informatics, Software Engineering, Statistics or another technical discipline.
  • Internship or early-career experience in the pharmaceutical or high-tech industry.
  • Strong programming skills in Python and/or R, with familiarity with SQL.
  • Familiarity with version control, software testing and documentation.
  • Familiarity with data structures, pipelines, APIs and cloud environments; approved AI integrations where relevant.
  • Basic ML and generative AI literacy and the ability to learn approved tools with review.
  • Working knowledge of software design, testing, debugging and maintainable code.
  • Ability to run specified AI tests and record model, prompt and data versions.
  • Ability to handle permitted data securely and recognize privacy, access and unsafe-AI-behavior risks.
  • Learning agility, structured problem-solving, accountability and effective collaboration.
  • Ability to explain software behavior, data flows and technical issues clearly to the intended audience.
.
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