Data Scientist (R-17873)

Key Responsibilities:

• Utilize the latest data science techniques across both supervised and unsupervised machine learning methodologies, Natural Language Processing, and graph analysis in automating and scaling internal business processes.
• Establish and maintain strong relationships with key business stakeholders.
• Engage clients and D&B colleagues to identify business needs and develop, implement, and manage solutions.
• Strong communication skills and the ability to simplify and explain complex concepts for stakeholders, clients, and senior leaders.
• Participate in all aspects of a modeling engagement, including design, data requirements, development, validation, calibration, documentation, approval, implementation, monitoring and reporting.
• Develop Global Analytic Solutions inclusive but not limited to statistical models based on D&B’s established best practices, methodologies, and tools.
• Research complex business issues and recommend solutions, including model inputs and end products, focusing on addressing specific customer needs and use cases.
• Serve as a Subject Matter Expert on predictive models within the team and with business users; consult with the business, as appropriate, on predictive modeling solutions.
• Enjoy and share academic literature and industry best practices. Identify business relevance of new methods and work with cross functional teams to create prototypes, assist in creating business cases and go to market strategies.
• Validate the performance of existing quantitative risk models and recommend changes when necessary.
• Drive timely retrieval of risk analytics data from existing systems to create algorithms that meet business needs.

Key Skills

• Master’s Degree or Ph.D. in a quantitative/applied field preferred (Statistics, Econometrics, Computer Science, Operations Research, Mathematics, Engineering).
• 7+ years’ operating successfully in data science roles, especially roles requiring cross-company collaboration and disciplined delivery of initiatives.
• Hands-on experience applying modern machine learning techniques.
• Ability to program in other statistical analysis languages, proficiency in programming languages (Python, R, SQL).
• Experience in feature engineering, automation, network analysis or Natural Language Processing.
• Experience working with outside clients on statistical engagements.
• Ability to manage multiple assignments, many of which have challenging timelines.
• Ability to work independently, as well as collaborate effectively in a team environment.
• Excellent communication and presentation skills.
• Proficiency in Microsoft Office Suite.
• Show an ownership mindset in everything you do. Be a problem solver, be curious and be inspired to take action. Be proactive, seek ways to collaborate and connect with people and teams in support of driving success.
• Continuous growth mindset, keep learning through social experiences and relationships with stakeholders, experts, colleagues and mentors as well as widen and broaden your competencies through structural courses and programs.
• Where applicable, fluency in English and languages relevant to the working market.