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
Ability to 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
Serve as a Subject Matter Expert on risk models within the Analytics team and with business users; consult with the business, as appropriate, on predictive modelling solutions
Develop AI Agents for business risk monitoring, 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 manage 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

Master’s degree or higher with concentration in a quantitative discipline such as (Math/Stat, Economics, Computer Science, Finance, Operations Research, etc.) with 5
8 years of experience in Data Science.
Proven experience on design and development of Risk models and frameworks
Experience in design and development of risk models is desirable.
Strong experience in Scorecard Development, 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
Strong client collaboration skills, including the ability to build and maintain relationships with clients
Ability to effectively communicate complex ideas to both a technical and non-technical audience.
Experience with Generative AI / Large Language Models (LLMs)
Strong knowledge of prompt engineering, prompt chaining, and structured LLM outputs
Familiarity with agent frameworks (e.g., LangChain, LangGraph or equivalent) is a strong plus

Senior Data Science Analyst

Key skills