Senior Data Scientist - Fraud

Equifax Enterprise Innovation Office is seeking a strong Data Scientist with subject matter expertise in data structures, analytics, algorithms/models, and computer science fundamentals to lead data preparation, analytics, and development of deployable solutions across multiple projects in the Fraud space.

Responsibilities

• Design and deploy advanced fraud and credit risk models to mitigate threats across the enterprise.
• Develop and productionize innovative GenAI and LLM-driven solutions for complex fraud detection.
• Develop customer fraud models with exposure to various fraud types (e.g., account takeover, identity theft, payment fraud).
• End-to-end design, development, and deployment of advanced machine learning, AI, and Generative AI models to power new product initiatives across the enterprise.
• Design and build novel algorithms to enhance data governance, quality, and metadata management capabilities, creating new ways to automatically know and manage data assets.
• Proactively collect, analyze, and interpret existing internal data and evaluate new, external data sources to identify and propose new product opportunities for business units.
• Act as a senior technical consultant and partner to global teams, helping them frame business problems, identify data-driven solutions, and overcome complex analytical challenges.
• Serve as a technical leader and mentor for junior data scientists and analysts, conducting code reviews, sharing best practices, and fostering a culture of innovation and excellence.
• Translate complex analytical findings and research outputs into clear, compelling presentations and strategic recommendations for diverse stakeholders, including senior leadership.
• Lead the development of projects with multiple deliverables, leveraging business and technical expertise.
• Lead the analytical strategy on critical technical capabilities for global company solutions.
• Work with key stakeholders to support development of proprietary analytical products and custom scores, effectively communicating the "so what" of the analysis using strong data visualizations and business language.
• Contribute to the evaluation of external data sources and data science capabilities, providing due diligence recommendations.

Requirements

• A Master’s or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field.
• 7-10+ years of hands-on experience building and deploying production-level data science solutions using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks.
• Proficient skills building models using packages including scikit learn, XGBoost, TensorFlow, PyTorch, Transformers, etc.
• 4+ years of experience in Python and its core data science libraries (e.g., Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow).
• 4+ years of experience working with massive (petabyte-scale) datasets using strong SQL and big data technologies (e.g., Spark, Dataflow, BigQuery, Snowflake) within a cloud environment (AWS, GCP-Preferred).
• Deep knowledge of classical machine learning, statistical modeling, and NLP, with a strong command of supervised and unsupervised learning, time-series analysis, and model validation techniques.
• Excellent verbal and written communication skills, with a proven ability to collaborate effectively with cross-functional teams and present complex topics to non-technical audiences.
• 1+ year of experience mentoring junior data scientists.
• 2+ years’ experience developing and leading the technical vision of an organization and working independently and closely with senior leadership.
• Experience with development and deployment of models in a cloud-based environment such as AWS or GCP.
• Background in, and an innate talent and passion for, trying new technologies and quickly assessing value and implementability within organizations.
• Hunger for innovation and ability to switch between multiple projects at once.

Skills

• Extensive experience in developing and deploying production-level Fraud and Credit risk models.
• Extensive experience in building, fine-tuning, and productionizing GenAI and LLM solutions (e.g., RAG, fine-tuning, LLM orchestration).
• Demonstrable, hands-on experience building and fine-tuning LLMs, developing Retrieval-Augmented Generation (RAG) systems, and understanding the MLOps lifecycle for GenAI.
• Prior experience working in the financial services industry (e.g., risk modeling, algorithmic trading, fraud detection, or compliance).
• A portfolio or past experience building models specifically for data management (e.g., data quality anomaly detection, PII identification, automated data cataloging).
• A history of publishing research in relevant AI/ML conferences or contributing to major open-source data science projects.
• Knowledge in graph mining and graph data models.
• Innate talent and passion for trying new technologies and quickly assessing value and implementability within organizations.
• Demonstrable experience with identity graphs and graph-related technologies is a plus.

Schedule

4 days of high-impact, in-office collaboration (Monday–Thursday) in Alpharetta, GA, paired with Friday flexibility to work remotely.

This role does not offer immigration sponsorship (current or future), including F-1 STEM OPT extension support. This is a direct-hire role and is not open to C2C or vendors.