Analyst II, Data Science (R-19484)

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. For over 180 years, 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 .

The Role: Dun & Bradstreet Technology and Corporate Services India LLP is looking for candidates to support the Data Science team in Trade and Credit Risk Analytics. The candidate needs to work closely with the team based in India and across a range of Analytics leaders who are located globally to fulfill delivery on a timely basis

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. Internally this position titled as Analyst II, Data Science All Dun & Bradstreet job postings can be found at https://jobs.lever.co/dnb. Official communication from Dun & Bradstreet will come from an email address ending in @dnb.com. Notice to Applicants : Please be advised that this job posting page is hosted and powered by Lever, a subsidiary of Employ Inc. Your use of this page is subject to Employ's Privacy Notice and Cookie Policy, which governs the processing of visitor data on this platform. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please visit https://bit.ly/3LMn4CQ.