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.