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Senior Data Scientist – Product Analytics
לא צויןמשרה מלאהProduct Analytics
Join a global technology company as a Senior Data Scientist, using advanced analytics, experimentation, and statistical methods to inform product strategy, understand user behavior, and guide business decisions.
תחומי אחריות
- Analyze large and complex datasets to address product and business problems.
- Apply quantitative analysis, statistical methods, experimentation, and data mining.
- Define and monitor product metrics, goals, and success measures.
- Identify product opportunities and levers that influence roadmaps and priorities.
- Design experiments and evaluate the impact of product initiatives.
- Analyze user behavior, trends, and long-term product performance.
- Translate complex analysis into clear recommendations and business insights.
- Partner with Product, Engineering, and other teams on product strategy and investment decisions.
- Lead data-driven projects from problem definition through recommendations and execution.
- Use AI tools to improve analytical workflow efficiency, quality, and effectiveness.
דרישות
- At least 8 years of experience in data science, analytics, or a related field.
- Strong SQL skills and programming or scripting experience with Python and/or R.
- Bachelor’s degree in computer science, mathematics, statistics, engineering, or another relevant quantitative or technical field.
- Strong quantitative and statistical problem-solving skills.
- Experience defining product metrics, designing and analyzing experiments, and translating findings into actionable insights.
- Experience leading data-driven projects from definition through execution.
- Strong understanding of user behavior, product ecosystems, and long-term product trends.
- Excellent communication and presentation skills.
- Ability to collaborate effectively with Product, Engineering, and cross-functional stakeholders.
יתרון
- Master’s or Ph.D. in a quantitative field.
- Experience integrating AI tools into analytical workflows.
- Knowledge of responsible AI practices, including risk assessment, bias mitigation, and quality reviews.
- Experience with prompt engineering, context engineering, agent orchestration, or related emerging AI technologies.
- Demonstrated commitment to developing AI skills and tracking emerging technologies.