Lead Data Scientist

Lead Data Scientist

Overview

Harnham is partnering with a national residential real estate organization specializing in single family rental homes. The organization manages an extensive portfolio across the United States, overseeing leasing, resident services, property maintenance, repairs, and renovations.

Data science supports decisions across the business, including improving occupancy and resident retention, assessing payment behavior, and forecasting property expenses. The analytics team works directly with leadership to identify financial opportunities, quantify their value, and implement solutions that deliver measurable results.

The Role

The Lead Data Scientist will develop predictive models that strengthen financial decision-making, improve risk assessment, and support property operations. Working within a small analytics team, you will independently lead projects from problem definition through implementation and present recommendations to senior and executive stakeholders.

Financial or risk modeling experience within a regulated industry is required, including direct experience defending models during external audits. Formal people management experience is not required.

Key Responsibilities

• Own analytical projects from stakeholder intake and scoping through data preparation, model development, validation, implementation, and monitoring.
• Develop models supporting payment behavior, collectability, resident renewals, renovation costs, and repair decisions.
• Apply gradient boosting, regression, segmentation, and other statistical and machine learning methods to financial, credit, and operational datasets.
• Gather, reconcile, clean, and validate data from internal and external sources.
• Evaluate model assumptions, conduct backtesting, and document methodologies, limitations, and results.
• Defend model design, assumptions, and validation approaches during external audits.
• Translate analytical findings into financial impact, including cost savings, revenue opportunities, and changes in risk exposure.
• Present recommendations and business cases to senior leaders and executive stakeholders.
• Partner with business teams to implement solutions and measure ongoing performance.
• Mentor junior colleagues on analytical methods, project structure, and model development.

Required Qualifications

• Eight or more years of experience in data science, predictive analytics, quantitative analysis, or advanced risk modeling.
• Financial or risk modeling experience within a regulated industry.
• Direct experience supporting external model audits and defending model methodology and results.
• Advanced Python and SQL proficiency, including at least three years of experience developing models in Python and writing complex SQL queries.
• Experience with gradient boosting models, including XGBoost or CatBoost.
• Strong mathematical and statistical knowledge, including probability, sampling, predictive modeling, and model validation.
• Experience working with financial or credit datasets and translating findings into measurable business outcomes.
• Demonstrated ability to independently deliver projects from initial problem definition through implementation.
• Strong presentation skills, including experience communicating technical findings to executive audiences.
• A bachelor’s degree in mathematics, statistics, economics, computer science, quantitative engineering, or a related discipline with substantial mathematical and statistical coursework.

Relevant experience may include credit risk, credit loss assessment, probability of default modeling, scorecards, payment behavior, credit attributes, mortgages, credit cards, banking, or credit bureau data.

Preferred Qualifications

• A master’s degree in data science or another quantitative discipline. A business analytics master’s degree alone does not replace the required mathematical and statistical foundation.
• Experience with portfolio risk management, loan scoring, or quantitative analysis of capital assets.
• Familiarity with model risk management practices and SR 11-7 guidelines.
• Experience with Snowflake and AWS services, including Amazon S3.
• Experience applying predictive analytics to residential real estate or related operational decisions.

Location and Compensation

The position offers a base salary of $140,000 to $150,000 annually and an annual bonus.

Tempe, Arizona is the preferred location. Candidates in Dallas, Texas or Atlanta, Georgia will also be considered.

The hybrid schedule requires office attendance Monday through Thursday. Atlanta candidates will follow this schedule. Tempe and Dallas candidates must be prepared to attend on the same schedule when office capacity becomes available.

Work Authorization

Applicants must be authorized to work in the United States without employer sponsorship now or in the future.

The employer cannot provide employment visa sponsorship, visa transfers, or immigration petition support, including H-1B, H-1B1, E-3, TN, O-1, and L-1 arrangements requiring employer support, or employer-sponsored permanent residency.

Applicants working under F-1 CPT, OPT, or STEM OPT must also meet the requirement to work without employer sponsorship now or in the future.