Risk Data Science Analyst (R-19979)
Dun & Bradstreet, Inc.This role is no longer listed. Dun & Bradstreet, Inc. took the posting down on Sep 21, so applying now won't reach anyone. It stays here for your saved list and any links you shared.
Match Score
Against your primary resumeLogin to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Protocol Intelligence
Data-driven signals on your job's competitivenessMid-level ML role in metro with known brand, generalist ML and LLM requirements drives high competition.
Role focuses on B2B credit risk and fraud, requiring domain-specific financial services expertise, so background fit is high.
Explicit 2–5 years, Master's degree, and specific ML, PySpark, and SQL requirements make shortlisting high.
Job Description
Structured overview of role & requirementsAbout This Role
Develop and implement B2B risk solutions, including standard and custom models for clients such as Fortune 500 companies.
Apply machine learning techniques including LLMs and prompt engineering to analyze structured and unstructured data for credit risk, fraud detection, and compliance.
Design, test, and deploy AI agents utilizing ML and NLP to provide real-time predictive risk insights and collaborate on new risk analytics business solutions.
Minimum Requirements
Master’s degree or higher in quantitative disciplines like Math/Statistics, Economics, Computer Science, Finance, or Operations Research.
2-5 years of experience in Data Science, with desirable experience in risk model development.
Strong programming skills in Python and PySpark; strong SQL skills with experience handling large datasets.
Work Experience Required: 2-5 years in Data Science; notice period: Not explicitly mentioned in the JD.
Ideal Candidate Profile
Experience developing and validating machine learning risk models for financial or B2B risk analytics contexts.
Ability to manage multiple projects under tight deadlines with both independent and collaborative work styles.
Strong communicative ability to explain complex technical concepts to technical and non-technical stakeholders effectively.