Verified Job
IT / Software / Data Analyst
Senior Director - Data Scientist
IT / Software / Data Analyst
#746726
Remote / WFH
ZEE - Technology & Innovation · Bengaluru, Karnataka ·
Job Description
Job description
Responsibilities:
• Lead, grow, and mentor a high-performing team of data scientists.
• 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 prioritise 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 rigour.
• 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.
• 10+ years of experience in Data Science or Analytics, with at least 2-3 years of people management experience.
• Experience in the search and recommendation side.
• Proven track record of building and deploying ML models in production at scale.
• Experience managing teams of 10+ data scientists 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, prioritise 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 personalisation, recommendations, ranking, or supply chain ML problems.
• Strong understanding of ML system performance trade-offs: latency, cost, and throughput.
Responsibilities:
• Lead, grow, and mentor a high-performing team of data scientists.
• 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 prioritise 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 rigour.
• 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.
• 10+ years of experience in Data Science or Analytics, with at least 2-3 years of people management experience.
• Experience in the search and recommendation side.
• Proven track record of building and deploying ML models in production at scale.
• Experience managing teams of 10+ data scientists 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, prioritise 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 personalisation, recommendations, ranking, or supply chain ML problems.
• Strong understanding of ML system performance trade-offs: latency, cost, and throughput.
