Director, Data Science & Strategic Analysis

Maymont Homes•Charleston, SC

1d•Onsite

About The Position

The Director of Data Science and Strategic Analysis is responsible for independently scoping, executing, and delivering investment-grade research that translates complex demographic, macroeconomic, and market data into specific, actionable capital deployment opportunities. The research will then form the foundation for advanced data science models, analytics, and decision-support tools that this role will build in conjunction with other colleagues. These tools will enable the organization to make faster, smarter, and more informed decisions primarily supporting investment decisions but also operational workflows. The role will partner closely with Investments, Asset Management, Finance, Revenue Management, and Executive Leadership, in particular the CEO and CIO of Maymont. The ideal candidate combines strong technical expertise in data science with financial and strategic thinking. This individual is equally comfortable building predictive models, analyzing investment opportunities, evaluating housing markets, and presenting analytical insights to executive leadership. Success in this role requires curiosity, business acumen, and the ability to transform complex data into actionable investment recommendations. More specifically, the candidate has the intellectual range to receive a thesis-level research question, such as “where should we be investing given the silver tsunami demographic shift?”, and return within two weeks with a geospatial analysis, investment thesis, and platform-specific recommendations to deploy capital. The person possesses investment judgment, not just analytical skills, and is fluent in AI-assisted development tools that allow them to build and ship the tools that make research actionable.

Requirements

Bachelor's or master's degree in Data Science, Statistics, Economics, Finance, Applied Mathematics, Computer Science, Engineering, or a related quantitative field.
Minimum of 8-10 years of experience in quantitative research, data science, or applied analytics, with demonstrated investment-side experience (investment committee exposure, memo writing, or capital deployment decisions preferred).
Strong understanding of statistical modeling, predictive analytics, forecasting, optimization techniques, clustering, regression, machine learning methodologies, and the model development lifecycle (training, validation, deployment, and monitoring).
Experience evaluating investment opportunities through quantitative analysis, financial modeling, forecasting, scenario analysis, and portfolio performance measurement.
Experience analyzing housing markets, rental pricing, acquisition underwriting, portfolio optimization, demographic trends, geospatial data, competitive intelligence, and macroeconomic indicators affecting residential real estate investments.
Advanced proficiency in Python and SQL, with experience using common data science and machine learning libraries (e.g., pandas, scikit-learn, and modern ML frameworks).
Experience leveraging Generative AI, Large Language Models (LLMs), and agentic workflows to automate research, generate market intelligence, and improve analytical efficiency.
Ability to frame ambiguous business problems, develop analytical approaches, and translate findings into strategic recommendations.
Ability to clearly communicate complex analytical concepts to technical and non-technical audiences.
Strong problem-solving skills and attention to detail.
Demonstrated ability to influence strategic business decisions through analytical insights.
Excellent communication, collaboration, and presentation skills with both technical and executive stakeholders.

Nice To Haves

Master's degree or PhD in Data Science, Statistics, Economics, Finance, Operations Research, or MBA with a quantitative focus.
Experience within real estate, private equity, investment management, asset management, or financial services.
Experience building and deploying predictive pricing, forecasting, or optimization models in production.
Experience utilizing geospatial analytics and external market data sources.
Experience with AWS cloud services and modern AI platforms.
Familiarity with Git, Agile development methodologies, and collaborative software development practices.

Responsibilities

Own a self-managed research roadmap driven by senior executive priorities, conducting independent deep-dive research sprints (typically 1–3 weeks) that deliver investment-grade analysis spanning full U.S. Housing platform: single-family rental, build-to-rent, senior housing, affordable housing, manufactured housing, and market-rate multifamily.
Translate completed research into deployed, dynamic tools within our proprietary analytics application. For example, by converting a geospatial demographic analysis into an interactive heat map tool that enables platform-level investment decisions. This requires fluency in AI-assisted development workflows (e.g., Claude Code or similar agentic coding environments) to move from research output to working product without dependence on a separate engineering team.
Analyze housing market trends, demographic shifts, competitive positioning, and macroeconomic factors impacting investment performance.
Translate complex analytical findings into concise recommendations for executive leadership and investment teams.
Collaborate with Data Engineering to ensure scalable, reliable, and trusted analytical datasets.
Evaluate emerging technologies, modeling techniques, and external data sources to continuously improve model performance, scalability, and analytical capabilities.
Perform other duties as assigned to support business objectives