Analyst II, Data Science (R-19979)

Dun & Bradstreet; Chennai District; On-site; INR 1,800,000 - 2,800,000; Full time

Job summary

Dun & Bradstreet is seeking a data scientist to shape risk analytics for global clients in a fast-paced environment. You will design and validate risk models, apply ML methods, and develop AI agents to provide predictive insights.

The role emphasizes collaboration with internal and external stakeholders, handling multiple client projects, and maintaining strong SQL data workflows. Based in India, the position offers opportunities to influence business decisions through data-driven risk

Qualifications

• Master’s degree or higher in a quantitative discipline (Math/Stats, Economics, Computer Science, Finance, OR), 2–5 years in Data Science.
• Experience in development of risk models is desirable.
• Experience applying ML models (XGBoost, LightGBM, RF, LR, NN, etc.).
• Strong programming skills in Python and PySpark for data manipulation and statistical analysis.
• Strong SQL skills and experience with large datasets.
• Ability to build and maintain client relationships and communicate complex ideas to technical and non-technical audiences.

Responsibilities

• Develop B2B Risk solutions including standard and custom offerings for Fortune 500 clients.
• Engage with internal/external clients and stakeholders across modelling engagements.
• Apply LLMs and prompt engineering to analyze large B2B datasets for risk, fraud, and compliance.
• Design, test, and deploy new risk signals from structured and unstructured data.
• Develop AI Agents for business using ML/NLP for real-time risk detection and predictive insights.
• Collaborate effectively in a team and manage multiple assignments with tight timelines.
• Partner with Dun & Bradstreet teams to create new risk analytics solutions.

Skills

Data Science

ML Models

Python

PySpark

SQL

Risk Modeling

Client Relations

Education

Master’s degree in quantitative field

Tools

XGBoost

LightGBM

Neural Networks

Random Forest

Logistic Regression

Decision Tree

Job description

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?

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.