Data Scientist

Summary

Join Dun & Bradstreet's Public Sector Advanced Analytics team as an Econometrician. You will leverage econometric and ML approaches with large datasets to develop solutions for public sector clients. Responsibilities include developing econometric and time series insights, creating publication-quality reports, and communicating results to various audiences. This role requires 4-7 years of experience, a degree in a quantitative field, and strong programming skills (Python/Pyspark). A comprehensive knowledge of econometric, time series, and ML modeling is essential. The ideal candidate will possess strong collaboration, communication, and project management skills.

Requirements

• 4 to 7 years of experience with a degree in Economics, Econometrics, Statistics, or Mathematics, with a quantitative specialization
• Programming (Python/Pyspark ) skills are required
• Comprehensive knowledge of econometric, time series and ML modeling
• Ability to work on an interdisciplinary and cross-functional team
• Strong collaboration and communication abilities (including writing) and project management skills
• Ability to effectively communicate complex ideas to both a technical and non-technical audience

Responsibilities

• Help development of solutions that provide global or country-specific econometrics and time series insights that address and solve public sector customer problems
• Develop approaches that leverage econometric, time series and ML methods blended with economic theory covering topics including but not limited to causal inference, climate risk and spatial economics for use as products
• Preparing drafts of publication quality economic reports/commentaries/papers based on public sector data science solutions, also contribute towards scalability and automation of such reports
• Communicate analytical results in terms that are meaningful to business managers and senior leadership internally and externally
• Participate in all aspects of ongoing modeling engagements, including design, development, validation, calibration, documentation, approval, implementation, monitoring, visualization, and reporting
• Develop a working knowledge of how current systems and data sources are used in existing predictive modeling projects; drive timely retrieval of analytics data from existing system to create algorithms that meet business needs

Preferred Qualifications

Hands-on experience with topics such as causal inference, climate risk modeling and spatial economics is a plus