Risk Data Science Analyst (R-19979)

Shape the Future with Dun & Bradstreet
At Dun & Bradstreet, we believe data has the power to create a better tomorrow. As a global leader in business decisioning data and analytics, we help companies worldwide grow, manage risk, and innovate. Since 1841, businesses have trusted us to turn uncertainty into opportunity. We’re a diverse, global team that values creativity, collaboration, and bold ideas. Are you ready to make an impact and help shape what’s next? Join us! Explore opportunities at dnb.com/careers.
Key Responsibilities:
Work on development of B2B Risk solutions which includes Standard and custom solutions catering to various clients including fortune 500 companies
Work with internal / external D&B clients and stakeholders; Participate in all aspects of a modelling engagement, including design, development, validation, calibration, documentation, approval, implementation, monitoring, and reporting
Applying LLMs, and prompt engineering to analyze large-scale, unstructured and structured B2B datasets (e.g., Company News, Corporate Annual Reports) for credit risk, fraud detection, and compliance.
Design, develop and test new risk signals to effectively identify risk patterns from structured and Unstructured data
Develop AI Agents for business deploying autonomous agents. These agents utilize Machine Learning (ML) and Natural Language Processing (NLP) to detect risk triggers, anomalies in real-time, shifting risk management from reactive reporting to predictive, actionable insights
Ability to work on multiple assignments, many of which with challenging timelines
Ability to work independently, as well as collaborate effectively in a team environment
Partner with internal D&B team to develop new business solutions in risk analytics
Key Skills:
What we are looking for:
Master’s degree or higher with concentration in a quantitative discipline such as (Math/Stat, Economics, Computer Science, Finance, Operations Research, etc.) with 2 - 5 years of experience in Data Science.
Experience in development of risk models is desirable.
Application of Machine Learning Models using techniques such as Xgboost, Light GBM, Random Forest, Logistic Regression, Decision Tree, Neural Networks etc.,
Strong programming skills with the ability conduct research utilizing Python and Pyspark to manipulate data and conduct statistical analysis.
Strong SQL skills and experience working with large datasets.
Ability to build and maintain relationships with clients.
Ability to effectively communicate complex ideas to both a technical and non-technical audience.
Preferred Skills:
Analytical mind and business acumen, especially in Financial Services Industry.
Working experience in applying modern machine learning techniques.
Passionate on stay abreast of cutting-edge ML algorithms, with good grasp of ML explain-ability methods.
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Work arrangement
Hybrid