CIAL Dun & Bradstreet is the leading commercial data and counterparty decisioning solutions provider across 43 countries in Latin America and the Caribbean.

As the exclusive Latin American partner of the Dun & Bradstreet Worldwide Network, we serve 20,000+ customers with insights on 600M+ companies globally, combining the power of a world-class data network with deep local expertise built over decades in the region.

You will be responsible for leading and driving strategic data science projects focused on developing data-driven indicators to support business and product decisions. 3+ years of experience as a Data Scientist or Data Analyst.
~ Proficient in Python (pandas, NumPy and scikit-learn libraries, advanced regular expressions, data pipelines), SQL (complex joins, stored procedures in T-SQL), and database management.
~ Experience in data mining, forecasting, and building predictive and statistical models.
~ Experience with Power BI or other data visualization tools.
~ Familiarity with the commercial data risk ecosystem (credit and/or suppliers),



ideally in a Latin American country.
~ Knowledge of machine learning, business-applied artificial intelligence, and process automation.
~ Education in engineering, mathematics, physics, computer science, or another quantitative field.

Develop data-driven insights and indicators to improve product and business decisions, such as customer forecasts, contract renewals, goal achievement likelihood, and product success probability.
Discover and leverage valuable data sources, automate extraction, transformation, and loading (ETL) processes, and perform data quality analysis (completeness, profiling, etc.).
Analyze large volumes of structured and unstructured data to uncover relevant patterns and trends.
Integrate AI solutions into business processes, ensuring data security and cybersecurity compliance.
Present insights and results through clear and effective visualizations (using Power BI or other tools).
Lead data scien

📌 Data Scientist (México)
🏢 Cial Dun & Bradstreet
📍 México