Sr Scientist - Business Owner, Data Automation
Amgen
Sr Scientist - Business Owner, Data Automation
Job Description
Career CategoryResearchJob DescriptionHOW MIGHT YOU DEFY IMAGINATION?If you feel like you're part of something bigger, it's because you are. At Amgen, our shared mission--to serve patients--drives all that we do. It is key to our becoming one of the world's leading biotechnology companies. We are global collaborators who achieve together--researching, manufacturing, and delivering ever-better products that reach over 10 million patients worldwide. It's time for a career you can be proud of.Sr. Scientist - Business Owner, Data Automation (Large Molecule Discovery Informatics)LiveWhat you will do Let's do this. Let's change the world. In this vital role, you will serve as the Business Owner for Data Automation within Large Molecule Discovery (LMD), helping identify, analyze, and develop well-defined digital laboratory and scientific-data automation opportunities across the LMD informatics ecosystem. You will focus on improving the scientific user experience, data capture, instrument integration, workflow efficiency, and the availability of structured, reusable scientific data across research operations. As a scientifically grounded partner to laboratory and technology teams, you will work closely with scientists, laboratory operations teams, software engineers, informatics specialists, automation experts, enterprise architects, and external partners to translate scientific workflow needs into clear business requirements, use cases, and solution recommendations. You will document current-state workflows and future-state needs so laboratory-generated data can move efficiently from instruments and research platforms into enterprise systems while maintaining data quality, traceability, interoperability, and usability. This position sits at the intersection of laboratory science, research informatics, and digital transformation. You will bring a strong understanding of scientific workflows and a practical ability to assess processes, evaluate automation options, and partner with technical implementation teams to deliver capabilities that improve how data is captured, managed, analyzed, and used across LMD. Recommendations and priorities will be developed in close partnership with, and reviewed by, LMD strategy and leadership teams. In this role, your core responsibilities include: Serve as the scientific business owner and subject matter partner for laboratory data automation initiatives within Large Molecule Discovery. Identify, assess, and develop automation opportunities, including supporting analyses, value cases, and roadmap inputs, for consideration and prioritization by LMD strategy and leadership teams. Conduct discovery and design-thinking activities with scientific users to map current-state workflows, identify pain points, and document future-state process needs, operating-model considerations, and success measures. Gather, document, and refine business requirements, user stories, workflow requirements, acceptance criteria, and delivery priorities for laboratory workflows, instrument integration, scientific data capture, and automation opportunities. Partner with scientists and laboratory teams to map instrument-to-ecosystem data flows, identify integration gaps, and develop data-model, metadata, and workflow requirements that support automated, traceable data movement. Research and evaluate laboratory and scientific informatics solutions, data and system architectures, and tradeoffs across LIMS, ELN, CDS, SDMS, IoT-enabled instruments, cloud platforms, data hubs, and related enterprise capabilities; summarize findings and recommendations for review. Collaborate with informatics, software engineering, data engineering, automation, and vendor teams to clarify solution options, integration patterns, and implementation considerations for digital laboratory capabilities. Support the development and implementation of automation capabilities that improve capture, handling, integration, and analysis of complex scientific data, including AI-enabled and emerging agentic laboratory workflows where appropriate. Support deployment, testing, validation, user acceptance, change management, and adoption of automation capabilities within laboratory environments; help ensure solutions align with real-world scientific workflows and user needs. Facilitate communication and working sessions across scientific, technical, and business teams; document recommended solutions, decisions, risks, dependencies, and outcomes in a clear and actionable manner. Identify manual processes that can benefit from automation, integration, standardization, or redesign, and contribute to scalable practices for instrument connectivity, automated data acquisition, data quality, consistency, completeness, and traceability. Track the effectiveness and realized value of implemented solutions through adoption, operational, and data-quality measures; surface continuous-improvement and scalable-reuse opportunities for LMD and partner organizations. Win What we expect of you We are all different, yet we all use our unique contributions to serve patients. The collaborative professional we seek is a scientific informatics partner with these qualifications. Basic Qualifications: Doctorate degree PhD OR PharmD OR MD [and relevant post-doc where applicable] and 7 years of directly related experience Or Master's degree and 8 years of directly related experience Or Bachelor's degree and 10 years of directly related experience Preferred Qualifications: Experience supporting biologics discovery, protein engineering, antibody discovery, assay development, laboratory operations, or related life-sciences R&D environments. Hands-on experience with laboratory workflows and laboratory informatics solutions, including LIMS, ELN, CDS, SDMS, scientific data-management platforms, or cloud-based research platforms. Implementation experience with any of the following laboratory solutions: Benchling, Revvity Signals, Dotmatics, LabVantage, LabWare, Scitara, TetraScience. Working knowledge of AWS, Python scripting, SQL, and numerical analysis for scientific or laboratory data. Experience with laboratory automation, instrument integration, APIs, or structured scientific-data capture and exchange. Familiarity with Agile delivery practices and iterative software development. Thrive What you can expect of us As we work to develop treatments that take care of others, we also work to care for our teammates' professional and personal growth and well-being. 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. Amgen offers a Total Rewards Plan comprising 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 including: 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 and bi-annual company-wide shutdowns Flexible work models, including remote work arrangements, where possible Apply now for a career that defies imagination Objects in your future are closer than they appear. Join us. careers.amgen.com .
Locations
- India - Hyderabad
Skills Required
- laboratory workflowsintermediate
- any of the following laboratory solutions: Benchlingintermediate
- AWSintermediate
- laboratory automationintermediate
- Agile delivery practicesintermediate
Required Qualifications
- Doctorate degree PhD OR PharmD OR MD [and relevant post-doc where applicable] and 7 years of directly related experience (experience, 7 years)
Preferred Qualifications
- Experience supporting biologics discovery, protein engineering, antibody discovery, assay development, laboratory operations, or related life-sciences R&D environments. (experience)
- Experience supporting biologics discovery, protein engineering, antibody discovery, assay development, laboratory operations, or related life-sciences R&D environments. (experience)
- Hands-on experience with laboratory workflows and laboratory informatics solutions, including LIMS, ELN, CDS, SDMS, scientific data-management platforms, or cloud-based research platforms. (experience)
- Hands-on experience with laboratory workflows and laboratory informatics solutions, including LIMS, ELN, CDS, SDMS, scientific data-management platforms, or cloud-based research platforms. (experience)
- Implementation experience with any of the following laboratory solutions: Benchling, Revvity Signals, Dotmatics, LabVantage, LabWare, Scitara, TetraScience. (experience)
- Implementation experience with any of the following laboratory solutions: Benchling, Revvity Signals, Dotmatics, LabVantage, LabWare, Scitara, TetraScience. (experience)
- Working knowledge of AWS, Python scripting, SQL, and numerical analysis for scientific or laboratory data. (experience)
- Working knowledge of AWS, Python scripting, SQL, and numerical analysis for scientific or laboratory data. (experience)
- Experience with laboratory automation, instrument integration, APIs, or structured scientific-data capture and exchange. (experience)
- Experience with laboratory automation, instrument integration, APIs, or structured scientific-data capture and exchange. (experience)
- Familiarity with Agile delivery practices and iterative software development. (experience)
- Familiarity with Agile delivery practices and iterative software development. (experience)
Responsibilities
- Let's do this. Let's change the world. In this vital role, you will serve as the Business Owner for Data Automation within Large Molecule Discovery (LMD), helping identify, analyze, and develop well-defined digital laboratory and scientific-data automation opportunities across the LMD informatics ecosystem. You will focus on improving the scientific user experience, data capture, instrument integration, workflow efficiency, and the availability of structured, reusable scientific data across research operations.
- This position sits at the intersection of laboratory science, research informatics, and digital transformation. You will bring a strong understanding of scientific workflows and a practical ability to assess processes, evaluate automation options, and partner with technical implementation teams to deliver capabilities that improve how data is captured, managed, analyzed, and used across LMD. Recommendations and priorities will be developed in close partnership with, and reviewed by, LMD strategy and leadership teams.
- Serve as the scientific business owner and subject matter partner for laboratory data automation initiatives within Large Molecule Discovery.
- Serve as the scientific business owner and subject matter partner for laboratory data automation initiatives within Large Molecule Discovery.
- Identify, assess, and develop automation opportunities, including supporting analyses, value cases, and roadmap inputs, for consideration and prioritization by LMD strategy and leadership teams.
- Identify, assess, and develop automation opportunities, including supporting analyses, value cases, and roadmap inputs, for consideration and prioritization by LMD strategy and leadership teams.
- Conduct discovery and design-thinking activities with scientific users to map current-state workflows, identify pain points, and document future-state process needs, operating-model considerations, and success measures.
- Conduct discovery and design-thinking activities with scientific users to map current-state workflows, identify pain points, and document future-state process needs, operating-model considerations, and success measures.
- Gather, document, and refine business requirements, user stories, workflow requirements, acceptance criteria, and delivery priorities for laboratory workflows, instrument integration, scientific data capture, and automation opportunities.
- Gather, document, and refine business requirements, user stories, workflow requirements, acceptance criteria, and delivery priorities for laboratory workflows, instrument integration, scientific data capture, and automation opportunities.
- Partner with scientists and laboratory teams to map instrument-to-ecosystem data flows, identify integration gaps, and develop data-model, metadata, and workflow requirements that support automated, traceable data movement.
- Partner with scientists and laboratory teams to map instrument-to-ecosystem data flows, identify integration gaps, and develop data-model, metadata, and workflow requirements that support automated, traceable data movement.
- Research and evaluate laboratory and scientific informatics solutions, data and system architectures, and tradeoffs across LIMS, ELN, CDS, SDMS, IoT-enabled instruments, cloud platforms, data hubs, and related enterprise capabilities; summarize findings and recommendations for review.
- Research and evaluate laboratory and scientific informatics solutions, data and system architectures, and tradeoffs across LIMS, ELN, CDS, SDMS, IoT-enabled instruments, cloud platforms, data hubs, and related enterprise capabilities; summarize findings and recommendations for review.
- Collaborate with informatics, software engineering, data engineering, automation, and vendor teams to clarify solution options, integration patterns, and implementation considerations for digital laboratory capabilities.
- Collaborate with informatics, software engineering, data engineering, automation, and vendor teams to clarify solution options, integration patterns, and implementation considerations for digital laboratory capabilities.
- Support the development and implementation of automation capabilities that improve capture, handling, integration, and analysis of complex scientific data, including AI-enabled and emerging agentic laboratory workflows where appropriate.
- Support the development and implementation of automation capabilities that improve capture, handling, integration, and analysis of complex scientific data, including AI-enabled and emerging agentic laboratory workflows where appropriate.
- Support deployment, testing, validation, user acceptance, change management, and adoption of automation capabilities within laboratory environments; help ensure solutions align with real-world scientific workflows and user needs.
- Support deployment, testing, validation, user acceptance, change management, and adoption of automation capabilities within laboratory environments; help ensure solutions align with real-world scientific workflows and user needs.
- Facilitate communication and working sessions across scientific, technical, and business teams; document recommended solutions, decisions, risks, dependencies, and outcomes in a clear and actionable manner.
- Facilitate communication and working sessions across scientific, technical, and business teams; document recommended solutions, decisions, risks, dependencies, and outcomes in a clear and actionable manner.
- Identify manual processes that can benefit from automation, integration, standardization, or redesign, and contribute to scalable practices for instrument connectivity, automated data acquisition, data quality, consistency, completeness, and traceability.
- Identify manual processes that can benefit from automation, integration, standardization, or redesign, and contribute to scalable practices for instrument connectivity, automated data acquisition, data quality, consistency, completeness, and traceability.
- Track the effectiveness and realized value of implemented solutions through adoption, operational, and data-quality measures; surface continuous-improvement and scalable-reuse opportunities for LMD and partner organizations.
- Track the effectiveness and realized value of implemented solutions through adoption, operational, and data-quality measures; surface continuous-improvement and scalable-reuse opportunities for LMD and partner organizations.
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