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Lead Data Scientist

$140k - $150k
Full-time

Harnham

Lead Data Scientist | Financial Risk & Advanced Analytics

Tempe, AZ (Preferred) | Dallas, TX | Atlanta, GA

Hybrid (4 days onsite, Monday-Thursday)

$140,000-$150,000 Base Salary

US Citizens & Green Card Holders Only

Are you looking for a role where your models don't sit on a shelf?

A highly respected organization is seeking a Lead Data Scientist to tackle complex financial risk problems, uncover multi-million-dollar business opportunities, and deliver analytics that directly influence executive decision-making. This is a high-visibility position where your work will be implemented, measured, and tied directly to business outcomes.

If you're someone who enjoys owning projects end-to-end, defending your analytical thinking, and seeing your work create measurable financial impact, this opportunity offers a rare combination of technical depth, business influence, and autonomy.

What You'll Do

  • Lead the development of predictive and quantitative models that drive critical business decisions.
  • Analyze large-scale financial and credit-related datasets to identify opportunities and risks.
  • Build, enhance, validate, and defend sophisticated risk models within a regulated environment.
  • Translate complex model outputs into clear financial recommendations and measurable business value.
  • Partner directly with senior leadership and executive stakeholders to present findings and influence strategy.
  • Own projects from initial problem definition through deployment and business adoption.
  • Support external audits and confidently explain model methodology, performance, and governance.

What We're Looking For

  • 8+ years of Data Science, Quantitative Analytics, Risk Analytics, or Modeling experience.
  • Advanced Python and SQL skills.
  • Strong foundation in statistics, mathematics, econometrics, or quantitative modeling.
  • Experience building machine learning models, including gradient boosting techniques.
  • Proven experience owning projects independently and delivering business results.
  • Strong communication skills with the ability to present technical concepts to non-technical executives.
  • Experience operating within regulated environments and supporting model audits.

Required Domain Experience

Candidates must have experience in financial or risk modeling environments, including areas such as:

  • Credit Risk Modeling
  • Probability of Default (PD) Modeling
  • Loss Forecasting or Credit Loss Assessment
  • Financial Risk Analytics
  • Scorecard Development
  • Payment Behavior Modeling
  • Credit Attribute Modeling
  • Banking, Lending, Credit Bureau, Mortgage, or Consumer Finance Analytics
  • Quantitative Risk Management

Preferred Education

Bachelor's degree in a quantitative discipline such as:

Mathematics

Statistics

Economics

Computer Science

Engineering

Related quantitative field

Master's degree in Data Science, Statistics, Applied Mathematics, Economics, or a similar quantitative field is highly preferred.

Why This Opportunity Stands Out

Unlike many Data Science positions, the impact here is immediate and visible.

You'll join a lean, high-performing team responsible for identifying and delivering opportunities worth millions of dollars annually. Leadership places significant value on data-driven decision-making, and the right person will have direct exposure to executives while maintaining ownership of mission-critical initiatives.

Ideal Candidate

This role is perfect for a senior individual contributor or lead-level data scientist who enjoys solving complex financial problems, building production-ready models, proving business value through analytics, and operating as a trusted strategic partner to leadership.

Confidential search. Qualified candidates will be contacted directly for additional details.

Base Salary: $140,000-$150,000 + comprehensive benefits

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

Work Authorization: US Citizen or Green Card Holder required

Vacancy posted 12 hours ago
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