- Relevant work experience in data analysis or related field. (e.g., as a statistician / data scientist / computational biologist / bioinformatician).
- Experience with statistical software (e.g., R, Python, Julia, MATLAB, pandas) and database languages (e.g., SQL).
- Experience articulating business questions and using mathematical techniques to arrive at an answer using available data.
- Experience translating analysis results into business recommendations.
- Demonstrated skills in selecting the right statistical tools given a data analysis problem.
- Demonstrated effective written and verbal communication skills.
- Demonstrated leadership and self-direction.
- Demonstrated willingness to both teach others and learn new techniques.
- Support the design, and implement data engineering solutions.
- Design, construct, test and optimize solutions.
- Work independently as well as in teams to deliver transformative solutions to clients.
- Manipulate data from database tables (Oracle, Redshift etc.).
- Develop ETL modules using ETL tools as well as custom developed application.
- Work with large, complex data sets. Solve difficult, non-routine analysis problems, applying advanced analytical methods as needed. Conduct end-to-end analysis that includes data gathering and requirements specification, processing, analysis, ongoing deliverables, and presentations.
- Build and prototype analysis pipelines iteratively to provide insights at scale. Develop comprehensive understanding of data structures and metrics, advocating for changes where needed for both products development and customers activity.
- Interact cross-functionally with a wide variety of people and teams. Work closely with engineers to identify opportunities for, design, and assess improvements.
- Make business recommendations (e.g. cost-benefit, forecasting, and experiment analysis) with effective presentations of findings at multiple levels of stakeholders through visual displays of quantitative information.
- Research and develop analysis, forecasting, and optimization methods to improve the quality of user facing products; example application areas include search quality, end-user behavioral modeling, and operational experiments.