What are the responsibilities and job description for the Head of Data Science, LearnX position at Karkidi?
Minimum qualifications : Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field, or equivalent practical experience.7 years of experience in analytics to solve product, business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 7 years of experience with a PhD degree.7 years of experience in statistical data analysis, and managing analytical projects.3 years of experience in people management within a technical leadership role.Preferred qualifications : Experience in scientific methodologies, data analysis, and experimental design.Experience in extracting large data sets and designing Extract Transform Load (ETL) processes.Experience in mentoring teams at all levels, from junior staff to executives, on data science projects.Ability to analyze problems and develop data-driven solutions.Ability to inspire and mentor data science teams with excellent leadership skills.Ability to communicate effectively with both technical and non-technical stakeholders, demonstrating excellent verbal and written communication skills.About the jobThe team's goal is to increase LearnX's impact and effectiveness by leveraging data science to drive key insights that influence strategy and product planning making.As a Head of Data Science, you will play a pivotal role in identifying or prioritizing key problems, developing, and investigating key metrics, and innovating on new methodologies or tools to match the accelerating pace of innovation.ResponsibilitiesPlan proactive and strategic contributions, and influence stakeholders through effective presentations and written communication.Work cross-functionally with stakeholders to influence, refine, and support product strategy and iteration cycles. Analyze and optimize metrics to drive insights and help decision-making processes.Develop, identify, and drive adoption of best practices across product data science teams, and build metrics, data quality strategy.Lead a team across multiple offices, time zones and partner with the pedagogy and user research teams to identify or validate key user behaviors.Attract, retain, and develop talent in data science. Provide mentorship, guidance, and professional development opportunities to team members.#J-18808-Ljbffr
