Principal Data Scientist
Amgen
Principal Data Scientist
Job Description
ABOUT AMGEN
What you will do
- Define and evangelise the multi-year AI-platform vision, architecture blueprints and reference implementations that align with Amgen’s digital-transformation and cloud-modernization objectives.
- Design and evolve foundational platform components—feature stores, model-registry, experiment-tracking, vector databases, real-time inference gateways and evaluation harnesses—using cloud-agnostic, micro-service principles.
- Implement robust MLOps pipelines (CI/CD for models, automated testing, canary releases, rollback) and enforce reproducibility from data ingestion to model serving.
- Embed responsible-AI and security-by-design controls—data-privacy, lineage tracking, bias monitoring, audit logging—through policy-as-code and automated guardrails.
- Serve as the ultimate technical advisor to product squads: codify best practices, review architecture/PRs, troubleshoot performance bottlenecks and guide optimisation of cloud resources.
- Partner with Procurement and Finance to develop TCO models, negotiate enterprise contracts for cloud/AI infrastructure, and continuously optimise spend.
- Drive platform adoption via self-service tools, documentation, SDKs and internal workshops; measure success through developer NPS, time-to-deploy and model uptime SLAs.
- Establish observability frameworks—metrics, distributed tracing, drift detection—to ensure models remain performant, reliable and compliant in production.
- Track emerging technologies (serverless GPUs, AI accelerators, confidential compute, policy frameworks like EU AI Act) and proactively integrate innovations that keep Amgen at the forefront of enterprise AI.
What we expect of you
- Master’s degree with 10-14 + years of experience in Computer Science, IT or related field OR Bachelor’s degree with 12-17 + years of experience in Computer Science, IT or related field
- Certifications on GenAI/ML platforms (AWS AI, Azure AI Engineer, Google Cloud ML, etc.) are a plus.
- 5-7 years in AI/ML, data platforms or enterprise software, including 3+ years leading senior ICs or managers.
- Experience in Biotechnology or pharma industry is a big plus
- Master’s degree in Computer Science, Data Science or MBA with AI focus.
- Familiarity with Agile methodologies and Scaled Agile Framework (SAFe) for project delivery.
Must-Have Skills
- Proven track record selecting and integrating AI SaaS/PaaS offerings and building custom ML services at scale.
- Expert knowledge of GenAI tooling: vector databases, RAG pipelines, prompt-engineering DSLs and agent frameworks (e.g., LangChain, Semantic Kernel).
- Proficiency in Python and Java; containerisation (Docker/K8s); cloud (AWS, Azure or GCP) and modern DevOps/MLOps (GitHub Actions, Bedrock/SageMaker Pipelines).
- Strong business-case skills—able to model TCO vs. NPV and present trade-offs to executives.
- Exceptional stakeholder management; can translate complex technical concepts into concise, outcome-oriented narratives.
- Excellent analytical and troubleshooting skills.
- Strong verbal and written communication skills
- Ability to work effectively with global, virtual teams
- High degree of initiative and self-motivation.
- Ability to manage multiple priorities successfully.
- Team-oriented, with a focus on achieving team goals.
- Ability to learn quickly, be organized and detail oriented.
- Strong presentation and public speaking skills.
Compensation
5-7
Locations
- Hyderabad, India
Salary
Salary not disclosed
Estimated Salary Rangehigh confidence
120,000 - 180,000 USD / yearly
Source: xAI estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Proven track record selecting and integrating AI SaaS/PaaS offerings and building custom ML services at scale.intermediate
- Expert knowledge of GenAI tooling: vector databases, RAG pipelines, prompt-engineering DSLs and agent frameworks (e.g., LangChain, Semantic Kernel).intermediate
- Proficiency in Python and Java; containerisation (Docker/K8s); cloud (AWS, Azure or GCP) and modern DevOps/MLOps (GitHub Actions, Bedrock/SageMaker Pipelines).intermediate
- Strong business-case skills—able to model TCO vs. NPV and present trade-offs to executives.intermediate
- Exceptional stakeholder management; can translate complex technical concepts into concise, outcome-oriented narratives.intermediate
- Excellent analytical and troubleshooting skills.intermediate
- Strong verbal and written communication skillsintermediate
- Ability to work effectively with global, virtual teamsintermediate
- High degree of initiative and self-motivation.intermediate
- Ability to manage multiple priorities successfully.intermediate
- Team-oriented, with a focus on achieving team goals.intermediate
- Ability to learn quickly, be organized and detail oriented.intermediate
- Strong presentation and public speaking skills.intermediate
Required Qualifications
- Master’s degree with 10-14 + years of experience in Computer Science, IT or related field OR Bachelor’s degree with 12-17 + years of experience in Computer Science, IT or related field (experience)
- Certifications on GenAI/ML platforms (AWS AI, Azure AI Engineer, Google Cloud ML, etc.) are a plus. (experience)
- 5-7 years in AI/ML, data platforms or enterprise software, including 3+ years leading senior ICs or managers. (experience)
- Experience in Biotechnology or pharma industry is a big plus (experience)
- Master’s degree in Computer Science, Data Science or MBA with AI focus. (experience)
- Familiarity with Agile methodologies and Scaled Agile Framework (SAFe) for project delivery. (experience)
Responsibilities
- Define and evangelise the multi-year AI-platform vision, architecture blueprints and reference implementations that align with Amgen’s digital-transformation and cloud-modernization objectives.
- Design and evolve foundational platform components—feature stores, model-registry, experiment-tracking, vector databases, real-time inference gateways and evaluation harnesses—using cloud-agnostic, micro-service principles.
- Implement robust MLOps pipelines (CI/CD for models, automated testing, canary releases, rollback) and enforce reproducibility from data ingestion to model serving.
- Embed responsible-AI and security-by-design controls—data-privacy, lineage tracking, bias monitoring, audit logging—through policy-as-code and automated guardrails.
- Serve as the ultimate technical advisor to product squads: codify best practices, review architecture/PRs, troubleshoot performance bottlenecks and guide optimisation of cloud resources.
- Partner with Procurement and Finance to develop TCO models, negotiate enterprise contracts for cloud/AI infrastructure, and continuously optimise spend.
- Drive platform adoption via self-service tools, documentation, SDKs and internal workshops; measure success through developer NPS, time-to-deploy and model uptime SLAs.
- Establish observability frameworks—metrics, distributed tracing, drift detection—to ensure models remain performant, reliable and compliant in production.
- Track emerging technologies (serverless GPUs, AI accelerators, confidential compute, policy frameworks like EU AI Act) and proactively integrate innovations that keep Amgen at the forefront of enterprise AI.
Tags & Categories
Principal Data Scientist
Amgen
Principal Data Scientist
Job Description
ABOUT AMGEN
What you will do
- Define and evangelise the multi-year AI-platform vision, architecture blueprints and reference implementations that align with Amgen’s digital-transformation and cloud-modernization objectives.
- Design and evolve foundational platform components—feature stores, model-registry, experiment-tracking, vector databases, real-time inference gateways and evaluation harnesses—using cloud-agnostic, micro-service principles.
- Implement robust MLOps pipelines (CI/CD for models, automated testing, canary releases, rollback) and enforce reproducibility from data ingestion to model serving.
- Embed responsible-AI and security-by-design controls—data-privacy, lineage tracking, bias monitoring, audit logging—through policy-as-code and automated guardrails.
- Serve as the ultimate technical advisor to product squads: codify best practices, review architecture/PRs, troubleshoot performance bottlenecks and guide optimisation of cloud resources.
- Partner with Procurement and Finance to develop TCO models, negotiate enterprise contracts for cloud/AI infrastructure, and continuously optimise spend.
- Drive platform adoption via self-service tools, documentation, SDKs and internal workshops; measure success through developer NPS, time-to-deploy and model uptime SLAs.
- Establish observability frameworks—metrics, distributed tracing, drift detection—to ensure models remain performant, reliable and compliant in production.
- Track emerging technologies (serverless GPUs, AI accelerators, confidential compute, policy frameworks like EU AI Act) and proactively integrate innovations that keep Amgen at the forefront of enterprise AI.
What we expect of you
- Master’s degree with 10-14 + years of experience in Computer Science, IT or related field OR Bachelor’s degree with 12-17 + years of experience in Computer Science, IT or related field
- Certifications on GenAI/ML platforms (AWS AI, Azure AI Engineer, Google Cloud ML, etc.) are a plus.
- 5-7 years in AI/ML, data platforms or enterprise software, including 3+ years leading senior ICs or managers.
- Experience in Biotechnology or pharma industry is a big plus
- Master’s degree in Computer Science, Data Science or MBA with AI focus.
- Familiarity with Agile methodologies and Scaled Agile Framework (SAFe) for project delivery.
Must-Have Skills
- Proven track record selecting and integrating AI SaaS/PaaS offerings and building custom ML services at scale.
- Expert knowledge of GenAI tooling: vector databases, RAG pipelines, prompt-engineering DSLs and agent frameworks (e.g., LangChain, Semantic Kernel).
- Proficiency in Python and Java; containerisation (Docker/K8s); cloud (AWS, Azure or GCP) and modern DevOps/MLOps (GitHub Actions, Bedrock/SageMaker Pipelines).
- Strong business-case skills—able to model TCO vs. NPV and present trade-offs to executives.
- Exceptional stakeholder management; can translate complex technical concepts into concise, outcome-oriented narratives.
- Excellent analytical and troubleshooting skills.
- Strong verbal and written communication skills
- Ability to work effectively with global, virtual teams
- High degree of initiative and self-motivation.
- Ability to manage multiple priorities successfully.
- Team-oriented, with a focus on achieving team goals.
- Ability to learn quickly, be organized and detail oriented.
- Strong presentation and public speaking skills.
Compensation
5-7
Locations
- Hyderabad, India
Salary
Salary not disclosed
Estimated Salary Rangehigh confidence
120,000 - 180,000 USD / yearly
Source: xAI estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Proven track record selecting and integrating AI SaaS/PaaS offerings and building custom ML services at scale.intermediate
- Expert knowledge of GenAI tooling: vector databases, RAG pipelines, prompt-engineering DSLs and agent frameworks (e.g., LangChain, Semantic Kernel).intermediate
- Proficiency in Python and Java; containerisation (Docker/K8s); cloud (AWS, Azure or GCP) and modern DevOps/MLOps (GitHub Actions, Bedrock/SageMaker Pipelines).intermediate
- Strong business-case skills—able to model TCO vs. NPV and present trade-offs to executives.intermediate
- Exceptional stakeholder management; can translate complex technical concepts into concise, outcome-oriented narratives.intermediate
- Excellent analytical and troubleshooting skills.intermediate
- Strong verbal and written communication skillsintermediate
- Ability to work effectively with global, virtual teamsintermediate
- High degree of initiative and self-motivation.intermediate
- Ability to manage multiple priorities successfully.intermediate
- Team-oriented, with a focus on achieving team goals.intermediate
- Ability to learn quickly, be organized and detail oriented.intermediate
- Strong presentation and public speaking skills.intermediate
Required Qualifications
- Master’s degree with 10-14 + years of experience in Computer Science, IT or related field OR Bachelor’s degree with 12-17 + years of experience in Computer Science, IT or related field (experience)
- Certifications on GenAI/ML platforms (AWS AI, Azure AI Engineer, Google Cloud ML, etc.) are a plus. (experience)
- 5-7 years in AI/ML, data platforms or enterprise software, including 3+ years leading senior ICs or managers. (experience)
- Experience in Biotechnology or pharma industry is a big plus (experience)
- Master’s degree in Computer Science, Data Science or MBA with AI focus. (experience)
- Familiarity with Agile methodologies and Scaled Agile Framework (SAFe) for project delivery. (experience)
Responsibilities
- Define and evangelise the multi-year AI-platform vision, architecture blueprints and reference implementations that align with Amgen’s digital-transformation and cloud-modernization objectives.
- Design and evolve foundational platform components—feature stores, model-registry, experiment-tracking, vector databases, real-time inference gateways and evaluation harnesses—using cloud-agnostic, micro-service principles.
- Implement robust MLOps pipelines (CI/CD for models, automated testing, canary releases, rollback) and enforce reproducibility from data ingestion to model serving.
- Embed responsible-AI and security-by-design controls—data-privacy, lineage tracking, bias monitoring, audit logging—through policy-as-code and automated guardrails.
- Serve as the ultimate technical advisor to product squads: codify best practices, review architecture/PRs, troubleshoot performance bottlenecks and guide optimisation of cloud resources.
- Partner with Procurement and Finance to develop TCO models, negotiate enterprise contracts for cloud/AI infrastructure, and continuously optimise spend.
- Drive platform adoption via self-service tools, documentation, SDKs and internal workshops; measure success through developer NPS, time-to-deploy and model uptime SLAs.
- Establish observability frameworks—metrics, distributed tracing, drift detection—to ensure models remain performant, reliable and compliant in production.
- Track emerging technologies (serverless GPUs, AI accelerators, confidential compute, policy frameworks like EU AI Act) and proactively integrate innovations that keep Amgen at the forefront of enterprise AI.
Tags & Categories