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Danaher Corporation Logo Danaher Corporation · IT - Software Development

Director, Applied Machine Learning

India, Bangalore · Job Posted April 23, 2026
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Job Description

This leader works in tight partnership with the MLOps team (deployment, monitoring, lifecycle automation) and the Data Platform team (standardized, governed, reliable data). The role also aligns closely with Digital & AI engineering to ensure clean handoffs and integration into customer-facing and internal experiences when needed. This position reports to the VP, Digital, Data & AI and is part of the Digital, Data & AI team and will be an on-site role.

Job Responsibility

  • Build and lead a new applied machine learning team, including hiring, coaching, career development, and setting a high-performance culture with clear delivery accountability
  • Establish the applied ML charter, operating model, and engagement pattern with data product teams to ensure ML work is prioritized by value and delivered as part of product roadmaps
  • Own model development end-to-end, including problem framing, feature strategy, model selection, training, evaluation, and iteration based on business and technical metrics
  • Partner with data product managers and domain stakeholders to define use cases, success measures, and adoption plans, ensuring solutions are usable and drive outcomes
  • Work closely with the MLOps team to productionize models with strong engineering rigor, including CI/CD, model registries, reproducible training, automated testing, monitoring, and retraining triggers
  • Collaborate with the data platform team to ensure ML solutions leverage trusted, standardized data assets and patterns (e.g., curated datasets, lineage, access controls, scalable pipelines)
  • Define and enforce best practices for applied ML delivery, including experimentation discipline, documentation, model governance, and responsible AI practices appropriate for the business context
  • Ensure models and workflows meet security, privacy, and compliance requirements, including operating effectively in SOX and GxP environments where applicable
  • Drive reuse and scale by developing shared components (feature patterns, evaluation templates, reference architectures) and reducing one-off model implementations
  • Provide senior technical leadership and pragmatic guidance to stakeholders on when ML is appropriate versus rules-based or analytical alternatives
  • Establish and report on applied ML performance and value metrics (e.g., model quality, drift, business lift, adoption, time-to-deploy), and drive continuous improvement

Requirements

  • Bachelor’s degree in computer science, engineering, data science, or a related field
  • advanced degree preferred
  • 10+ years of experience in applied machine learning and/or data science, with 4+ years in people leadership roles
  • Proven experience building and scaling teams that deliver machine learning models into production with measurable impact
  • Strong applied foundation in ML methods and evaluation, with the judgment to select practical approaches and manage tradeoffs
  • Demonstrated ability to partner effectively with MLOps and platform teams to operationalize models reliably and securely
  • Experience working in a product delivery model, collaborating with product managers, engineers, and business stakeholders in a matrixed environment
  • Strong communication skills, including the ability to explain technical tradeoffs, align on priorities, and influence senior stakeholders
  • Working knowledge of cloud-based data and ML stacks and modern engineering practices (CI/CD, testing, observability, security by design)
  • Delivering ML solutions in regulated environments (e.g., life sciences, healthcare, biotech), including familiarity with SOX and/or GxP expectations
  • Developing and operating model monitoring, drift detection, and retraining pipelines at scale
  • Working with feature stores, model registries, experimentation platforms, and governance workflows
  • Applying ML to enterprise domains such as supply chain, manufacturing, quality, service, pricing, or customer experience
  • Deploying and evaluating genai or NLP solutions in production, with appropriate controls and measurement

Nice to have

  • Delivering ML solutions in regulated environments (e.g., life sciences, healthcare, biotech), including familiarity with SOX and/or GxP expectations
  • Developing and operating model monitoring, drift detection, and retraining pipelines at scale
  • Working with feature stores, model registries, experimentation platforms, and governance workflows
  • Applying ML to enterprise domains such as supply chain, manufacturing, quality, service, pricing, or customer experience
  • Deploying and evaluating genai or NLP solutions in production, with appropriate controls and measurement
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