Data Scientist

We are seeking an experienced and driven Data Science expert to join the Analytical Capabilities team, where you will help power Equifaxs AI and analytical offerings. In this role, you will build innovative tools and perform in-depth analysis to support customer decision points across the consumer lending lifecycle. By leveraging advanced mathematical and statistical approaches, you will develop robust, reusable, and highly automated solutions.

In this position, you will analyze complex data sources using various programming languages to inform business strategy and deliver insights for both internal and external stakeholders. You will develop models to test hypotheses and quantify the impact of decisions under various scenarios. Your ability to communicate technical results will be essential in driving profitability and translating business requirements into scalable, data-driven products.

What youll do:

• Design, build, and optimize predictive models and machine learning pipelines to support business strategy and customer decision points.
• Extract, clean, and feature-engineer large-scale structured and unstructured datasets using statistical and computing tools.
• Translate complex business requirements into automated, reusable analytical components and scalable data products.
• Present technical findings, model performance metrics, and business impact to mid-level leadership and external stakeholders.
• Conduct code and model reviews for peer and junior data scientists to maintain high accuracy, reproducibility, and governance standards.

• Bachelors or Masters degree in a STEM field (Data Science, Statistics, Computer Science, or related quantitative discipline).
• 5–7 years of hands-on experience in predictive modeling, statistical analysis, and machine learning deployment in real-world applications.
• Advanced proficiency in Python and SQL for data manipulation, analysis, and algorithm development.
• Demonstrated experience in feature development, model performance evaluation, and hyperparameter optimization.
• Practical hands-on experience working within enterprise cloud platforms (GCP, AWS, or Azure).

• Prior domain experience in financial services, specifically credit risk, marketing analytics, or collections.
• Experience handling large-scale datasets using PySpark, BigQuery, Snowflake, or Hadoop platforms.
• Google Cloud Platform (GCP) certifications (e.g., Professional Data Engineer or Professional Machine Learning Engineer).
• Proficiency with Git version control, CI/CD pipelines, and MLOps best practices.