Manager - Data Analytics

As a Data Strategy Manager, your role involves developing and executing a data strategy to align with product development and business objectives. Your responsibilities include fostering data-driven decision-making throughout the product lifecycle, overseeing data collection, analysis, and interpretation, and ensuring timely delivery of accurate reports and visualizations. You will integrate new data sources and technologies, define product-specific KPIs, and provide insights to support strategic product initiatives. Key Responsibilities: - Develop and execute a data strategy aligned with product and business objectives - Foster data-driven decision-making throughout the product lifecycle - Oversee data collection, analysis, and interpretation to provide actionable insights - Ensure timely delivery of accurate reports, dashboards, and visualizations - Integrate new data sources and technologies for product analytics - Define and track product-specific KPIs and metrics - Provide insights and recommendations for strategic product enhancements - Assess the impact of data-driven projects on product success and user experience - Advocate for the value of data analytics in driving product innovation and growth - Communicate complex data concepts to non-technical stakeholders - Collaborate with cross-functional teams to address product data needs - Share success stories and case studies of data-driven product improvements - Stay updated on industry trends in product analytics and technology - Foster innovation in analytical techniques and methodologies - Encourage continuous learning and development within the data team Qualifications: - Education: Bachelor's degree in Computer Science, Statistics, Data Science, or related field (Advanced degree preferred) - Experience: Extensive experience in data analytics or business intelligence with a proven leadership track record - Skills: Successful implementation of data strategies, statistical analysis, data modeling, data visualization, proficiency with analytics tools (e.g., SQL, Python, R, Tableau, Power BI), leadership, communication, presentation, translating data insights into business recommendations, knowledge of ML, data governance, data quality, and data protection practices.,