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- Improve the appeal process (e.g. account disabling, feature limits, etc.)
- Understand user needs and identify areas for improvement
- Find a balance between protecting the platform, supporting user voices, managing revenue, and following regulations
- Get buy-in from the business on your ideas and help make them happen
- You'll also work with data partners to improve how we measure and track progress in this area, ensuring we stay on course
- Collect, organize, analyse, and summarize statistical data to support the design and development of Our Client’s products
- Use your expertise in quantitative analysis, data mining, and data visualization to uncover insights about user behavior across both consumer and business products
- Collaborate with Product and Engineering teams to identify trends, solve problems, and spot opportunities
- Influence and support product decisions and launches through data-driven insights
- Contribute to a variety of projects, including product operations, exploratory analysis, product strategy, and data infrastructure
- Tackle problems of varying complexity, applying data analysis to assess key factors and generate solutions
- Apply sound judgment in choosing appropriate methods and techniques to solve problems effectively
- Conduct tactical (feature-level) and strategic (team goals and objectives) data analyses to steer team direction
- Develop a strategic narrative grounded in analytical insights and priorities
- Identify key metrics to define success for products and features
- Quantitative analysis and data mining on complex datasets
- Data querying languages (e.g., SQL), scripting languages (e.g., Python), or statistical software (e.g., R, SAS, Matlab)
- Applied statistics and experimentation, such as A/B testing in a professional setting
- Communicating analysis results to influence product and leadership strategy
- Machine learning techniques
- ETL (Extract, Transform, Load) processes
- Relational databases and large-scale data processing using distributed systems
- Quantitative techniques like clustering, regression, pattern recognition, and statistical analysis
- Master's degree in Computer Science, Engineering, Analytics, Math, Economics, Physics, or a related field
- Experience with quantitative analysis and data mining on complex data sets.
- Proficient in SQL (data querying)
- Proficient in Python (scripting)
- Experience with statistical or mathematical software like R, SAS, or Matlab
- Experience with applied statistics or experimentation (like A/B testing) in a real-world setting
- Knowledge of quantitative techniques like clustering, regression, pattern recognition, and statistics
