Senior Machine Learning Leader

2 days ago

Bengaluru, Karnataka, India beBeeMachineLearning Full time ₹ 1,00,000 - ₹ 1,20,000
Job Title: Senior Machine Learning Leader

This is an exciting opportunity for a seasoned professional to lead our machine learning team in developing and deploying intelligent systems that drive business growth.

  • Lead a high-performing team of data scientists and engineers to build, deploy, and monitor machine learning models.
  • Collaborate with senior leaders to define and prioritize impactful machine learning initiatives.
  • Develop and implement strategies for platformization, system architecture decisions, and ML lifecycle improvements.
  • Ensure model scalability, performance, and cost efficiency while upholding best practices in experimentation and statistical rigor.
  • Guide the team in reading and implementing state-of-the-art research and facilitate build vs. buy decisions.
  • Lead root cause analysis for critical production issues and improve system observability, documentation, and service uptime.
Requirements
  • Master's degree in Machine Learning, Statistics, Computer Science, or a related quantitative field.
  • 8+ years of experience in Data Science or Analytics, with at least 2–3 years of people management experience.
  • Proven track record of building and deploying machine learning models in production at scale.
  • Experience managing teams of 4+ data scientists/engineers and delivering across cross-functional charters.
  • Deep expertise in machine learning algorithms, experimental design, and performance monitoring.
  • Strong coding skills (Python, SQL) and familiarity with Big Data technologies like Spark, Hive, or Redshift.
  • Ability to translate business needs into technical solutions, prioritize roadmaps, and estimate effort accurately.
  • Strong communication and stakeholder management skills with a bias for action and clarity in execution.
Preferred Requirements
  • Experience leading machine learning initiatives in B2C or e-commerce settings.
  • Contributions to internal tools, open-source libraries, or research publications.
  • Experience in personalization, recommendations, ranking, or supply chain machine learning problems.
  • Strong understanding of machine learning system performance trade-offs.

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