Description

Role
The Global Data Office is at the center of our enterprise's data-driven transformation. We are seeking an innovative and experienced Senior Data Scientist to join our Data Innovations & Solutions team. In this role, you will be a technical leader responsible for researching, building, and deploying cutting-edge AI, machine learning, and Generative AI solutions. You will tackle some of our most complex challenges, from creating new data-driven products to inventing novel algorithms that help us govern and understand our vast data ecosystem. If you thrive on solving complex problems at scale, mentoring talented junior colleagues, and turning raw EFX data into tangible business value, this is the role for you.

What you'll do

• 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.

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
• Experience with Agentic AI and writing algorithms
• Proficient skills building models using packages including scikit learn, XGBoost, Tensorflow, PyTorch, Transformers
• 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

What could set you apart

• Demonstrable, hands-on experience building and fine-tuning LLMs, developing Retrieval-Augmented Generation (RAG) systems, and understanding the MLOps lifecycle for GenAI.
• 1-3 years of Leadership/Team management experience
• 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).
• 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.
• 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 implement ability within organizations.