Data Scientist
At a glance
Mid-level Data Science role at Amgen. Hyderabad · full-time.
Pay not stated Data Scientist salary
dataroles summary, based on the employer's posting.
What you'll do
- Build statistical, forecasting, machine-learning and AI models for Clinical Development questions, using guidance and agreed baselines
- Prepare permitted clinical data, check its quality and record its origins, evaluation splits and model versions
- Write reproducible R, Python and SQL code, then test models and review AI results for errors and unsupported claims
- Work with clinical, data science, programming and statistics partners to shape usable analyses and reviewed handoffs
What you bring
- A bachelor’s degree plus two years of related work, or a master’s degree
- Strong R or Python skills, familiarity with SQL and data handling, and experience with statistical, forecasting or ML methods
- Knowledge of reproducible workflows, testing, documentation and model validation, or the ability to build clinical development knowledge quickly
Who this fits
This mid-level role suits someone who can own agreed analysis deliverables while seeking guidance and human review for clinical use. You’ll collaborate with teams across regions and handle permitted clinical data under approved tools and methods.
From the employer
Career Category
ClinicalJob Description
As a Data Scientist, you will develop statistical, machine-learning and AI models for assigned Clinical Development questions with guidance from experienced colleagues. You will align analyses with study objectives, produce reproducible code and explain assumptions, findings and limitations. You will build pharmaceutical and clinical-data knowledge while using approved tools and methods with appropriate scientific and human review.
Responsibilities
· Apply statistical, stochastic and forecasting methods to questions involving study design, trial execution and monitoring.
· Apply standard ML and AI methods with guidance, comparing results with agreed baselines.
· Prepare permitted clinical datasets and document provenance, quality checks and evaluation splits.
· Develop reproducible R, Python and SQL code; record model, data and assigned prompt versions.
· Run agreed model tests and check AI outputs for errors, unsupported claims and limitations.
· Collaborate across regions with Data Science, Clinical Development, Programming, Statistics and data-platform partners.
· Test outputs with CfDA, GBS and GDO, seeking human review before clinical use.
· Understand end-user analytical needs and improve the usability of analytical outputs.
· Prepare reviewed findings, reproducible handoffs, user guidance and training materials.
Accountabilities
· Deliver reliable analyses and models that support decisions within assigned Clinical Development work.
· Protect sensitive data, use approved AI services and escalate unsafe or unreliable outputs.
· Contribute reusable analyses, evaluation cases, modeling components and documented workflows.
· Take ownership of agreed deliverables while escalating risks, assumptions and limitations.
· Maintain the quality, reproducibility and usability of code and outputs.
· Deliver validated analyses, models and documentation on time.
· Recommend modeling approaches and make code, testing and documentation decisions within agreed standards.
Basic Qualifications
· 5-9 over all experience
· Bachelor’s degree and 2 years of related experience or Master’s degree
Preferred Qualifications
· Master’s degree in Mathematics, Statistics, Operations Research, Computer Science or another scientific or technical discipline.
· Internship or early-career experience in the pharmaceutical or high-tech industry.
· Strong capability in R and/or Python, with familiarity with SQL and data manipulation.
· Familiarity with reproducible workflows, version control, testing and documentation.
· Experience with statistical, simulation, forecasting or ML methods, with exposure to AI evaluation where relevant.
· Basic ML and generative AI literacy and the ability to learn approved tools with review.
· Working knowledge of statistical modeling, uncertainty, model validation and interpretation.
· Understanding of Clinical Development, or the ability to build this knowledge quickly.
· Ability to recognize AI errors, privacy risks and limits of use when handling permitted clinical data.
· Learning agility, quantitative reasoning, accountability and effective cross-functional communication.
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