Manager, Data Science (Product Analytics)
IndiaFull-TimeManager
Salary not disclosed
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Job Details
- Required Skills
- HadoopProduct AnalyticsSparkMentoringData visualizationStakeholder management
Requirements
- Proven experience in experimental design, statistics, and analysis of large-scale user or product data
- Prior experience managing or leading a team of data scientists or analysts in a product-driven environment
- Strong background in product analytics, experimentation frameworks, and user behavior analysis
- Experience working with large-scale data systems and tools such as Hadoop, Hive, Presto, or Spark
- Ability to translate complex data insights into clear, actionable recommendations for cross-functional stakeholders
- Strong collaboration skills with product, engineering, and research teams in fast-paced environments
- Demonstrated commitment to equity, inclusion, and inclusive leadership practices
- Excellent communication, mentoring, and stakeholder management abilities
- Strong critical thinking, curiosity, and ability to operate in ambiguous and complex environments
- Experience with data visualization or experimentation tools such as Superset or GrowthBook is a plus
- Alignment with mission-driven work and openness to contributing in a collaborative, open knowledge ecosystem
Responsibilities
- Lead and manage a team of approximately seven data scientists, ensuring high-quality delivery of product analytics insights and reporting
- Oversee the timely production of accurate quantitative analysis to support product strategy, feature evaluation, and impact measurement
- Partner with product managers, engineers, and research teams to embed data-informed decision-making across product development cycles
- Design and improve experimentation frameworks, measurement systems, and product health metrics to support scalable analysis
- Collaborate with data engineering and experimentation platform teams to build accessible and reliable data products
- Guide the development of dashboards, reports, and visualization tools that improve data accessibility and interpretation
- Mentor team members in statistical thinking, storytelling with data, stakeholder communication, and technical development
- Ensure consistency, rigor, and accuracy in analytical processes, balancing speed with methodological robustness
- Foster a collaborative, inclusive, and high-performing team culture grounded in trust and accountability
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