Head of Machine Learning

2 hours ago

Bengaluru, Karnataka, India Mynd Full time ₹ 20,00,000 - ₹ 25,00,000 per year

Responsibilities:

  1. ML Architecture, Strategy & Innovation

  2. Define and drive the ML strategy across business domains, identifying high-impact opportunities for automation, optimization, and prediction.

  3. Architect scalable ML systems and reusable frameworks that support real-time inference, batch processing, and continuous learning.

  4. Lead the evaluation and adoption of cutting-edge ML techniques (e.g., foundation models, causal inference, reinforcement learning) to solve complex business problems.

  5. End-to-End ML Lifecycle Ownership

  6. Lead the design, development, and deployment of advanced supervised and unsupervised models for use cases such as churn prediction, demand forecasting, fraud detection, and dynamic pricing.

  7. Own the full ML lifecycle: from problem framing and data exploration to model training, validation, deployment, and monitoring.

  8. Champion best practices in experimentation, reproducibility, and responsible AI.

  9. Cross-Functional Leadership & Business Impact

  10. Partner with senior stakeholders across Sales, Customer Service, Finance, Supply Chain, and Fulfillment to define and prioritize ML initiatives aligned with strategic goals.

  11. Translate ambiguous business challenges into well-scoped ML solutions with measurable ROI.

  12. Serve as a technical advisor to executive leadership on AI/ML trends, risks, and opportunities.

  13. MLOps, Governance & Infrastructure

  14. Lead the design and implementation of robust MLOps pipelines using tools like DataRobot

  15. Ensure scalable, secure, and compliant deployment of models in cloud-native environments (AWS, Azure, GCP).

  16. Establish governance frameworks for model versioning, monitoring, retraining, and auditability.

  17. Data Engineering & Feature Platform Design

  18. Collaborate with data engineering teams to define and evolve enterprise-wide feature stores, data contracts, and real-time data pipelines.

  19. Drive innovation in feature engineering, leveraging domain knowledge and advanced statistical techniques.

  20. Mentorship, Collaboration & Thought Leadership

  21. Mentor junior ML engineers and data scientists, fostering a culture of technical excellence and continuous learning.

  22. Contribute to internal knowledge sharing, technical design reviews, and ML community engagement.
  23. Publish whitepapers, present at conferences, or lead internal workshops on emerging ML technologies.

Qualifications:

Required:

  • 8-10  years of experience in machine learning product management, AI engineering, or applied data science, with a strong foundation in software engineering.
  • Proven experience deploying and scaling ML models in production environments using modern MLOps practices.
  • Deep understanding of cloud ML platform capabilities (DataRobot is preferred)
  • Strong communication skills with the ability to influence technical and non-technical stakeholders.

Preferred:

  • Experience leading ML initiatives in enterprise domains such as Finance, Sales, or Supply Chain.
  • Familiarity with advanced ML techniques (e.g., transformers, graph neural networks, time series forecasting, causal modeling).
  • Exposure to enterprise platforms such as Salesforce, Oracle.
  • Graduate degree (MS or PhD) in Computer Science, Machine Learning, Statistics, or a related field. Role & responsibilities

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