Senior Manager, Data Science

Job Description

We are seeking a Senior Manager, Data Science to lead the Forecasting team within the AI & Data Science organization. This role will be accountable for setting the data science strategy, developing and leading a high-performing team, and partnering product, program, operations, commercial, manufacturing, supply chain, finance, and other business stakeholders to deliver enterprise forecasting, uncertainty-aware decision support, scenario planning, and AI-enabled decision automation capabilities across Amgen. As a senior data science leader, this role blends people leadership, scientific rigor, modern AI fluency, and product-minded execution. The Senior Manager will identify high-impact forecasting and operational decision opportunities, shape them into an actionable portfolio, guide rapid prototype-to-production delivery, and ensure solutions are adopted, measured, governed, and continuously improved based on real-world performance and business value. The role is well suited to a leader who has built and managed data science or AI teams, delivered production-grade AI/ML solutions with measurable business impact, and can operate effectively in ambiguous, cross-functional environments. This leader will help Amgen advance AI-native forecasting and decision-support capabilities while supporting the company's "every patient, every time" mandate.

Job Responsibility

Lead, coach, and develop a team of data scientists, AI/ML scientists, and analytics professionals, establishing clear priorities, high standards, career development plans, and an inclusive, accountable team culture.
Define and own the data science roadmap for enterprise forecasting, simulation, scenario planning, uncertainty quantification, predictive analytics, LLM-enabled applications, and AI-assisted decision support aligned to Amgen's planning, supply, commercial, manufacturing, operations, and patient-focused priorities.
Partner with senior business, product, program, operations, commercial, manufacturing, supply chain, finance, engineering, and AI stakeholders to translate ambiguous planning and decision challenges into prioritized data science initiatives with clear outcomes and measurable value.
Establish rigorous standards for forecast quality, model validation, experimentation, evaluation frameworks, guardrails, explainability, auditability, reproducibility, model monitoring, drift detection, and responsible AI practices in high-impact and regulated business contexts.
Create and maintain measurement frameworks to evaluate forecast accuracy, uncertainty calibration, decision quality, operational efficiency, reliability, user adoption, and business impact
lead build-measure-learn cycles that improve solutions based on real-world performance.
Serve as a senior advisor to stakeholders by communicating forecasts, uncertainty, model assumptions, trade-offs, risks, and recommendations in a clear, actionable way for both technical and executive audiences.
Manage the team portfolio, roadmap trade-offs, resourcing, stakeholder expectations, delivery risks, and dependencies across data science, engineering, product, and business teams.
Identify reusable methods, patterns, platforms, and governance practices that accelerate forecasting and AI decision-support delivery across Amgen and reduce duplication across teams.
Research and evaluate emerging open-source, vendor, and internal tools related to forecasting, decision intelligence, LLMs, AI agents, MLOps, model evaluation, and AI governance for potential application to Amgen business problems.
Promote strong data science craft, including scientific rigor, code quality, documentation, peer review, reproducibility, ethical AI use, operational excellence, and effective collaboration with engineering and business partners.

Requirements

12+ years of professional experience delivering data science, machine learning, forecasting, AI, analytics, or decision-support solutions that created measurable business value.
7+ years of experience managing, leading, or formally developing data science, machine learning, AI, analytics, or cross-functional technical teams.
Demonstrated experience setting data science strategy, prioritizing a portfolio of work, managing stakeholder expectations, and leading teams through ambiguous, high-impact business problems.
Deep experience with forecasting, predictive modeling, statistical modeling, probabilistic or Bayesian methods, uncertainty quantification, scenario analysis, experimentation, or optimization.
Experience partnering with machine learning engineering, software engineering, product, program, or platform teams to move models and analytics capabilities from prototype into production or scaled business use.
Experience with modern AI systems, including LLM-powered applications, AI agents, retrieval or information-retrieval systems, evaluation frameworks, guardrails, and human-in-the-loop operating patterns.
Strong analytical and technical fluency with Python, R, SQL, or equivalent tools, and familiarity with modern data science and ML frameworks such as scikit-learn, PyTorch, TensorFlow/JAX, Spark, MLflow, Airflow/Prefect/Dagster, or equivalent technologies.
Familiarity with cloud platforms, enterprise data platforms, model deployment patterns, MLOps, model monitoring, reproducibility, governance, security, privacy, and responsible AI practices.
Strong communication and executive-influence skills, including the ability to explain complex methods, forecast uncertainty, assumptions, model risks, and business implications to technical and non-technical audiences.
Demonstrated ability to hire, coach, mentor, and grow technical talent while fostering collaboration, inclusion, accountability, and a high bar for scientific and delivery excellence.

Nice to have

Experience leading forecasting, demand planning, commercial analytics, supply chain, manufacturing, operations, or decision intelligence teams in biotech, pharma, healthcare, retail, consumer goods, or other complex regulated or operational environments.
Knowledge of healthcare commercial concepts such as payer/provider dynamics, formulary access, coverage, patient access, channel dynamics, epidemiology, product lifecycle considerations, or launch planning.
Experience building or leading teams that delivered production ML/AI products, internal decision-support tools, dashboards, workflow applications, or autonomous/semi-autonomous AI capabilities used by non-technical stakeholders.
Experience with AI agent architectures, multi-system orchestration, tool/function calling, retrieval-augmented generation, MCP or similar integration patterns, and LLM evaluation or serving approaches in production or enterprise settings.
Experience designing guardrails, model/agent evaluation suites, A/B tests, offline and online metrics, auditability, explainability, fairness, risk management, and governance controls for high-impact AI/ML systems.
Experience using AWS or equivalent cloud services such as S3, Redshift, SageMaker, EMR, Kinesis, Lambda, EC2, EKS/ECS, or comparable GCP/Azure services.
Experience managing roadmaps, budget/resource trade-offs, vendor or platform partnerships, stakeholder governance forums, or communities of practice for data science or AI delivery.
Track record of influencing senior leaders, shaping enterprise AI/data science standards, and scaling reusable approaches across multiple products, functions, or business domains.
Publications, patents, conference presentations, open-source contributions, or other evidence of thought leadership in data science, forecasting, AI systems, LLMs, MLOps, or decision intelligence.

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Senior Manager, Data Science - Forecasting

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