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Director, Applied Machine Learning

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Danaher Corporation

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Location:
India , Bangalore

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Category:
IT - Software Development

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Contract Type:
Not provided

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Salary:

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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

Additional Information:

Job Posted:
April 23, 2026

Employment Type:
Fulltime
Work Type:
On-site work
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