-Basic Knowledge of software development principles and architecture.
-Good analytical and problem-solving abilities.
-Ability to break down and understand complex business problems, define a solution, and implement it using advanced quantitative methods.
-Familiarity with programming for data analysis; ideally Python, SQL, or R.
-Solid oral and written communication skills, especially around analytical concepts and methods.
-Great work ethic and intellectual curiosity.
-Knowledge of Cloud technologies such as AWS or Google Knowledge of any relational database such as My SQL.
-Must be a team player with excellent communication and problem-solving skills and have experience working with customers across teams
The ideal candidate should have a degree in a quantitative field [website] mathematics, computer science, physics, economics, engineering, statistics, operations research, quantitative social science, etc.).
-Strong problem-solving skills with an emphasis on product development.
-Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights from large data sets.
-Experience working with and creating data architectures and data models.
-Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
-Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests, and proper usage, etc.) and experience with applications.
-Excellent written and verbal communication skills for coordinating across teams.
- Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
- Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques, and business strategies.
- Assess the effectiveness and accuracy of new data sources and data gathering techniques.
- Develop custom data models and algorithms to apply to data sets.
- Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting, and other business outcomes.
- Develop company A/B testing framework and test model quality.
- Coordinate with different functional teams to implement models and monitor outcomes.
- Develop processes and tools to monitor and analyze model performance and data accuracy.