Job Overview
Location
Chennai - India
Job Type
Full-time
Category
Data Science
Date Posted
July 5, 2026
Full Job Description
đź“‹ Description
- • Lead the development and deployment of advanced data science models to enhance Dun & Bradstreet’s business decisioning platforms, leveraging structured and unstructured data from global sources.
- • Design and implement machine learning pipelines that improve the accuracy of business risk scoring, creditworthiness predictions, and company profile enrichment across international markets.
- • Collaborate with cross-functional teams including product, engineering, and data engineering to translate business requirements into scalable analytical solutions.
- • Analyze large-scale datasets containing billions of business records to identify patterns, anomalies, and predictive signals that drive actionable insights for enterprise clients.
- • Utilize statistical modeling techniques such as regression, clustering, classification, and time-series analysis to solve complex business problems in commercial intelligence.
- • Own the end-to-end lifecycle of data science projects—from hypothesis formulation and data exploration to model validation, deployment, and performance monitoring.
- • Apply natural language processing (NLP) techniques to extract structured insights from unstructured business documents, including financial statements, news articles, and regulatory filings.
- • Ensure model integrity and compliance with data governance standards, including data privacy regulations and internal audit requirements.
- • Document model architecture, assumptions, and performance metrics to enable reproducibility and knowledge sharing across global teams.
- • Stay current with advancements in data science, machine learning, and artificial intelligence, and evaluate emerging tools and techniques for potential adoption within the organization.
- • Present technical findings and business impact metrics to non-technical stakeholders through clear visualizations and storytelling techniques.
- • Mentor junior analysts and data scientists, fostering a culture of continuous learning and high-quality analytical output.
- • Work closely with data engineers to optimize data pipelines and ensure high-quality, timely data inputs for modeling efforts.
- • Contribute to the evolution of the company’s data science roadmap by identifying new use cases and opportunities for predictive analytics in commercial decision-making.
- • Participate in agile sprints and iterative development cycles to deliver incremental value to product teams and end users.
🎯 Requirements
- • Advanced degree in Data Science, Statistics, Computer Science, Economics, or a related quantitative field
- • Minimum 5 years of hands-on experience building and deploying machine learning models in a business analytics environment
- • Proficiency in Python and SQL, with experience using libraries such as scikit-learn, pandas, and PySpark
- • Demonstrated experience with cloud-based data platforms (e.g., AWS, Azure, or GCP)
- • Strong understanding of data governance, model validation, and regulatory compliance in enterprise settings
- • Experience working with global, multi-source business data including corporate financials, ownership structures, and risk indicators
🏖️ Benefits
- • Competitive salary and performance-based bonus structure
- • Comprehensive health, dental, and vision insurance coverage
- • Paid time off and flexible work arrangements
- • Professional development stipend for courses, certifications, and conferences
Skills & Technologies
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About Dun & Bradstreet, Inc.
Dun & Bradstreet is a global provider of commercial data, analytics and insights on businesses. The company maintains a database of more than 500 million corporate entities worldwide, offering credit reports, risk management tools, sales and marketing intelligence, supply chain data, and compliance solutions. Founded in 1841, it serves enterprises, financial institutions and governments seeking to assess business creditworthiness, identify opportunities and mitigate risk. Its offerings are delivered through cloud-based platforms and APIs, integrating with customer systems to support informed decision-making across finance, procurement and regulatory functions.
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