WHY WE WORK AT DUN & BRADSTREET

Dun & Bradstreet unlocks the power of data through analytics, creating a better tomorrow. Each day, we are finding new ways to strengthen our award-winning culture and accelerate creativity, innovation and growth. Our 6,000+ global team members are passionate about what we do. We are dedicated to helping clients turn uncertainty into confidence, risk into opportunity and potential into prosperity. Bold and diverse thinkers are always welcome. Come join us! Learn more at dnb.com/careers.
As a Data Scientist, you are expected to have the below qualifications and to fulfill the following responsibilities:

EDUCATION:

• Bachelors degree is required.
• Advanced degree in physics, math, computer science or similarly quantitative field - Preferred.
• Doctorate physics, math, computer science or similarly quantitative field - Preferred.

QUALIFICATIONS NEEDED:

• Full proficiency of ML development cycle: data gathering and transformation experimenting, training, tuning and testing productionalization, deployment and inference monitoring and retraining.
• Experience operating successfully in data science roles, especially as a member of cross-functional teams.
• Experience with solving broad range of analytic problems utilizing supervised/unsupervised learning, NLP, graphs, unstructured data analysis, Gen AI/LLM.
• Intellectual honesty and common sense.
• Comfort with ambiguity and pressure of fast paced environment.
• Strong data engineering skills to independently overcome common bottlenecks and friction points.

• Convert internal and clients’ business cases into a machine learning framework.
• Find the right technique, run full ML development cycle.
• Clearly explain and defend results of your work to technical audiences and in laymen’s terms.
• Independently identify business needs, generate ideas on how to address them.
• Efficiently balance independent work with team collaboration.
• Be able to run several projects in parallel.
• Be proficient in managing your time to meet deadlines.
• Be ready to do what it takes to deliver.
• Work jointly with cross-functional business units.
• Stay abreast of academic research and industry best practices.
• Share your knowledge across the team. Learn from others.