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# Data Scientist

- **Company:** Amgens Mission Of Serving Patients
- **Location:** Salt Lake City, UT, United States
- **Experience:** Expert
- **Salary:** $134,168.0 - $181,522.0
- **Contract:** Permanent contract
- **Skills:** Agile Methodology, Artificial Intelligence, Data Analysis, Information Engineering, Data Infrastructure, Data Visualization, Digital Assets, Digital Data, Systems Analysis, Python (Programming Language), Power BI, Software Engineering, SQL Databases, Systems Integration, Tableau (Software), Data Processing, Scripting, ReactJS, Information Technology, Data Management, Spotfire, GPT, Data Pipelines, TIBCO (Software), Databricks, Programming Languages
- **Published:** September 23, 2026
- **Apply:** https://www.careerbuilder.com/job-details/sr-data-scientist-process-modeling-salt-lake-city-ut--948e1492-7ab5-4f43-a3a3-588040d614b4

## About the Role

Doctorate degree

OR

Master's degree and 2 years of Data Science or Engineering experience

OR

Bachelor's degree and 4 years of Data Science or Engineering experience

OR

Associate's degree and 8 years of Data Science or Engineering experience

OR

High School diploma or GED and 10 years of Data Science or Engineering experience

Preferred Qualifications:

 * MS degree or doctorate in an engineering or scientific field with demonstrated capability in application of data science.
 * Chemical Engineering modeling, drug-substance, tech-transfer experience
 * 3+ years of work experience in biotechnology role (e.g. process engineer, scientist, data scientist)
 * Experience with AGILE software development methodologies
 * Experience with Databricks, coding languages (Python, SQL), AI Tools (Chat GPT, Codex, GenieCode), React, and BI tools (Spotfire/Tableau/Power BI)
 * Experience in developing AI applications for process development workflows and integrating AI solutions into process development business processes
 * Experience with process modeling for cell-culture, harvest,and purification scale-up
 * Demonstrated performance in application of digital tools to solve analytical problems
 * Demonstrated effective written and verbal communication skills
 * Demonstrated leadership and self-direction. Demonstrated willingness to both teach others and learn new techniques as well as manage team members and deliverables., Agile Programming Methodologies, Analysis Skills, Artificial Intelligence (AI), Biotech and Pharmaceutical, Business Intelligence Software, Business Model, Business Processes, Cancer, Cell Cultures, Change Management, Communication Skills, Cross-Functional, Data Analysis, Data Management, Data Modeling, Data Processing, Data Science, Data Visualization, Disease, Drug Development, Engineering, Forecasting, High School Diploma, Inflammatory Diseases, Knowledge Modeling, Leadership, Manufacturing, Medical Products, Medicine, Obesity, Oncology, Performance Analysis, Performance Management, Power BI, Presentation/Verbal Skills, Problem Solving Skills, Process Analysis, Process Development, Process Engineering, Process Improvement, Process Modeling, Product Development, Product/Service Launch, Python Programming/Scripting Language, SQL (Structured Query Language), Software Development, Strategic Planning, Systems Analysis, Tableau, Team Lead/Manager, Team Player, Technical Support, Tibco
Spotfire, Training/Teaching, Writing Skills

## Description

Let's do this. Let's change the world. In this vital role you will join the Digital Process and Systems Analysis group within Process Development plays a key role in providing data infrastructure, platforms, and analytics to support engineering teams across our network, ensuring state-of-the-art data insights to drive innovation and efficiency for the commercialization and optimization of Amgen's pipeline. This group also supports key priorities and the development of strategies for digital process development for both biologic and synthetic moieties.

This position is part of a team advancing biologics drug-substance modeling and the digitization of tech-transfer workflows. As new modeling capabilities are developed and information is parameterized, opportunities abound to deploy modeling tools to enable early insights and phase-appropriate decision-making. These capabilities support drug substance development, technology transfer, manufacture, and life-cycle management across Amgen's global network of manufacturing and development sites.

This role will contribute to the success of Drug Substance Technology organization by helping to digitize workflows to drive improvements in technology transfer timelines and ensure right-first-time success. As a Sr. Data Scientist, you will evaluate and improve Amgen's digital assets, collaborating as part of a multi-disciplinary team of scientists and engineers on a wide range of problems. The position will lead to solutions for open-ended "no single right answer" challenges, including modeling, data pipelines, and visualizations to support drug substance development, tech transfer, and manufacturing. The Sr. Data Scientist will also be responsible for determining, defining, and tracking performance improvements.

Responsibilities:

 * Leverage tech-transfer and process-modeling domain knowledge to drive implementation of digital technologies
 * Digital Product Ownership of tech-transfer digital applications, focused initially on expansion of digital process-modeling for the DS network
 * Change management to integrate tech transfer digital applications into product teams
 * Development of data products to enable FAIR data for tech transfer/NPI
 * Enable AI Agentic workflows for DST modeling assets
 * Collaborate with digital product teams to build and integrate modeling assets into business processes for DST
 * Interact cross-functionally with a wide variety of people and teams. Work closely with engineers to identify opportunities for process design improvements for Amgen products
 * Make business recommendations (cost-benefit, forecasting, scenario analysis) with effective presentation of findings to varied levels of stakeholders
 * Contribute to establishing a robust data science strategy by providing recommendations related to process monitoring, data engineering, analytic platforms, analysis plans, and other relevant areas of data science


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