Risk Analytics Modeler/Statistician
- Short Hills, NJ
Why We Work at Dun & Bradstreet
We are at a transformational moment in our company journey - and we're so excited about it. Each day, we are finding new ways to strengthen our award-winning culture, and to accelerate creativity, innovation and growth. Our purpose is to help customers improve business performance with Dun & Bradstreet's Data Cloud and Live Business Identity, and we're wildly passionate and committed to this purpose. So, if you're looking to make an immediate impact at a company that welcomes bold and diverse thinking, come join us!
The Predictive Analytics Modeling Statistician within our Analytics team is an exciting role that utilizes a mix of deep analytical skills, financial industry knowledge and client/stakeholder relationship management to build tools and analytics products.
We welcome creative and inquisitive individuals with the flexibility to learn and apply new methodologies.
- Work with internal / external D&B clients and stakeholders to identify their business needs and develop, implement, and manage solutions
- Participate in all aspects of a modeling engagement, including design, development, validation, calibration, documentation, approval, implementation, monitoring, and reporting
- Research complex business issues and recommend solutions, including model inputs and end products
- Grow into a Subject Matter Expert over time on predictive models within the Analytics team and with business users; consult with the business, as appropriate, on predictive modeling solutions
- Develop a solid working knowledge of how current systems and data sources are used in existing predictive modeling projects; Drive timely retrieval of risk analytics data from existing systems to create algorithms that meet business needs
- Manage multiple assignments, many of which with challenging timelines
- Work independently, as well as collaborate effectively in a team environment
- Master's degree with concentration in a quantitative discipline such as (Math/Stat, Economics, Computer Science, Finance, Operations Research, etc.).
- 2+ years of relevant experience.
- Strong programming skills with experience conducting research utilizing Pyspark, Python, R, Scala, SAS to manipulate data and conduct statistical analysis.
- Knowledgeable with SQL (PySpark or similar).
- Experience collaborating with clients, including building and maintaining relationships.
- Experience effectively communicating complex ideas to both a technical and non-technical audience
- Hands on experience with very large datasets.
- Strong analytical mind and business acumen, especially in financial services industry experience in underwriting, risk, statistical modeling roles.
- Proven working experience in Credit Risk modeling.
- Solid understanding of machine learning and explainability.
- Proven working experience in applying modern machine learning techniques.
- Strong technical writing skills.
Dun & Bradstreet is an Equal Opportunity Employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, creed, sex, age, national origin, citizenship status, disability status, sexual orientation, gender identity or expression, pregnancy, genetic information, protected military and veteran status, ancestry, marital status, medical condition (cancer and genetic characteristics) or any other characteristic protected by law.
We are committed to Equal Employment Opportunity and providing reasonable accommodations to applicants with physical and/or mental disabilities. If you are interested in applying for employment with Dun & Bradstreet and need special assistance or an accommodation to use our website or to apply for a position, please send an e-mail with your request to TalentAcquisitionTeam@dnb.com. Determination on requests for reasonable accommodation are made on a case-by-case basis.
Please note that all Dun & Bradstreet job postings can be found at https://dnb.wd1.myworkdayjobs.com/Careers and all communication from Dun & Bradstreet will come from an email address ending in @dnb.com.
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