Lead Data Scientist

Lead Data Scientist — Financial Risk Modeling

Location: Tempe, AZ preferred | Dallas, TX or Atlanta, GA considered

Work Model: Hybrid — 4 days onsite, Monday–Thursday

Compensation: $140,000–$150,000 base + 10% bonus

Work Authorization: U.S. Citizens and Green Card holders only

Apply promptly! A high volume of applicants is expected for the role as detailed below, do not wait to send your CV.

About the Company

This established organization operates in a highly regulated environment where advanced analytics and quantitative modeling play a meaningful role in financial decision-making and risk management.

The Data Science team works on high-impact problems where models are expected to move beyond experimentation and deliver measurable business outcomes. The environment is particularly well suited to Data Scientists who want ownership of their work, visibility with senior leadership, and the opportunity to see their models implemented and tied directly to business results.

About the Role

This is a hands-on Lead Data Scientist position focused on financial and risk modeling.

You'll independently own complex modeling initiatives from initial problem definition through development, validation, implementation, and measurement of business impact. The work involves large financial and credit datasets, advanced statistical modeling, Python, SQL, and translating model outputs into clear financial recommendations.

A critical component of this position is model defensibility. You'll present methodology and results to executive stakeholders and must be comfortable supporting external audits and defending the assumptions, methodology, validation, and performance of your models.

This is a Lead-level individual contributor position. Direct people management isn't required, but you'll be expected to drive projects independently, provide technical leadership, and support other members of the team.

Key Responsibilities

• Build, evaluate, and validate quantitative models supporting financial and risk decisions
• Develop models using Python, SQL, statistical techniques, and gradient boosting approaches
• Work with large-scale financial, credit, payment, and behavioral datasets
• Own complex Data Science initiatives from problem definition through implementation
• Translate model performance into measurable financial and dollar impact
• Develop and validate credit risk, loss, probability, behavioral, or comparable financial models
• Present technical methodology, model results, and recommendations to senior and executive stakeholders
• Support external model audits and clearly defend methodology, assumptions, validation, and results
• Evaluate model performance and identify opportunities for improvement
• Partner with stakeholders to translate business problems into quantitative solutions
• Provide technical leadership and support to other Data Scientists
• Help identify and quantify high-value opportunities with potentially significant financial impact

Must Haves

• 8+ years of Data Science, quantitative modeling, or closely related experience
• Significant finance and risk modeling experience within a regulated industry
• Advanced Python and SQL
• Strong statistical and mathematical foundation
• Deep data modeling and quantitative modeling experience
• Experience developing and evaluating gradient boosting models
• Experience with credit risk, financial risk, loss modeling, or comparable regulated modeling problems
• Experience working through external audits and defending models
• Ability to independently own modeling projects end-to-end
• Strong communication and presentation skills
• Experience presenting technical work and model results to senior or executive stakeholders
• Ability to connect model outputs to measurable financial impact
• Undergraduate degree in Mathematics, Statistics, Economics, xhyhwjd Computer Science, Quantitative Engineering, or another strongly quantitative discipline

Nice to Have

• Master's degree in Data Science, Statistics, Mathematics, Economics, Computer Science, or another quantitative discipline
• Credit risk or credit loss modeling
• Probability of Default (PD) modeling
• Scorecard development
• Payment behavior or credit attribute modeling
• Mortgage, home lending, credit card, banking, or credit bureau data experience
• Experience quantifying large-scale financial opportunities
• Previous technical leadership or mentoring experience

Why Join

• Own models from initial problem definition through implementation and measurable business results
• Work on quantitative problems tied directly to significant financial opportunities
• Join a smaller team where individual technical contributions are highly visible
• Present your work directly to senior and executive stakeholders
• Operate as a Lead-level technical contributor without having to move into people management
• Work in an environment where rigorous modeling, statistical judgment, and model defensibility matter
• See your models implemented rather than ending as research or recommendations