Senior Data Scientist - Fraud

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

Qualified candidates will have a passion for data science, mathematics, statistics, AI/Machine learning, data gathering, and experience in financial modeling. The ideal candidate will come from a fraud background, deeply understanding the importance of fraud in the credit data space.

We believe great things happen when teams connect. Our schedule is built around 4 days of high-impact, in-office collaboration (Monday–Thursday) , paired with Friday Flexibility to wrap up your week remotely.

This role reports to our office Alpharetta, GA office.

This position 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.

What You’ll Do

• 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 our data governance, quality, and metadata management capabilities, creating new ways to automatically know and manage our data assets.
• Proactively collect, analyze, and interpret existing internal data and evaluate new, external data sources to identify and propose new product opportunities for our business units.
• Act as a senior technical consultant and partner to global teams, helping them frame their business problems, identify data-driven solutions, and overcome their most 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 or 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.
  
  

What Experience You Need

• 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 to work 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. You should have 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 to lead data science to continued success into the future
• 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.
  
  

What Could Set You Apart

• 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 model
• 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.