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Data Science Manager

Data Science Manager

HyreSnapBangalore
19 days ago
Job description

Responsibilities :

  • Lead, grow, and mentor a high-performing team of data scientists and ML engineers.
  • Own all data science systems and models in your charter, from strategy to deployment and monitoring.
  • Collaborate with senior product, engineering, and business leaders to define and prioritize impactful DS initiatives.
  • Drive platformization, system architecture decisions, and ML lifecycle improvements across the charter.
  • 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 RCA for critical production issues and improve system observability, documentation, and service uptime.

Requirements :

  • Master's degree (PhD preferred) 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 ML models in production at scale.
  • Experience managing teams of 4+ data scientists / engineers and delivering across cross-functional charters.
  • Deep expertise in ML 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.
  • Bonus Points For :

  • Experience leading ML 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 ML problems.
  • Strong understanding of ML system performance trade-offs : latency, cost, and throughput.
  • (ref : hirist.tech)

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