Data Scientist

Location Charleston - 997 Morrison Drive, Suite 402

Business Our Growth, Your Opportunity At Maymont Homes, our success starts with people, our residents and our team. We are transforming the single-family rental experience through innovation, quality, and genuine care. With more than 20,000 homes across 47+ markets, 25+ build-to-rent communities, and continued expansion on the horizon, we are more than a leader in the industry—we are a company that puts people and communities at the heart of everything we do.

As part of Brookfield, Maymont Homes is growing quickly and making a lasting impact. We are also proud to be Certified by Great Place to Work, a recognition based entirely on feedback from our employees. This honor reflects the culture of trust, collaboration, and belonging that makes Maymont a place where people thrive.

Join a purpose-driven team where your work creates opportunity, sparks innovation, and helps families across the country feel truly  Description
** This position is onsite at 997 Morrison Dr, Charleston SC
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* Primary Responsibilities:

The Data Scientist leverages advanced analytics, statistical modeling, machine learning, and AI to solve complex business challenges and enable data-driven decision-making across the organization. This role develops, validates, and deploys predictive and statistical models using Python, while also performing hands-on data analysis, data extraction, and ad hoc reporting using Python, SQL, and Excel.

Working closely with cross-functional business partners, the Data Scientist translates business questions into analytical solutions, delivering actionable insights that support strategic initiatives and operational decision-making. The role is responsible for owning the end-to-end modeling lifecycle, including problem definition, data preparation, model development, validation, performance evaluation, and communication of results to both technical and non-technical audiences.

The ideal candidate combines strong expertise in data science, machine learning, and statistical analysis with practical proficiency in Python, SQL, and Excel. They are intellectually curious, analytical, and comfortable working with complex datasets to uncover meaningful insights. Success in this role requires the ability to quickly develop domain expertise in the housing industry, collaborate effectively with business stakeholders, and translate technical findings into clear, impactful recommendations that drive business value.

Skills &

Competencies:

Qualifications:

Bachelor's degree in Data Science, Statistics, Economics, Finance, Applied Mathematics, Computer Science, Engineering, or a related quantitative field.
3+ years of experience in data science, analytics, or applied quantitative work.

Strong problem-solving skills and attention to detail.

Strong Python, SQL, and Excel skills, with the ability to handle ad hoc data requests from business partners.

Excellent communication, collaboration, and presentation skills with both technical and business audiences.

Familiarity with Git, Agile development methodologies, and collaborative software development practices.

Preferred Qualifications:

Experience with in 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.

Essential

Skills:

Data Science & Machine Learning:
Solid working knowledge of statistical modeling, predictive analytics, regression, and core machine learning methods, with hands-on experience building models.

Problem Solving:
Ability to take a defined business problem, develop an analytical approach, and translate findings into clear, usable recommendations for business partners.

Excel & Ad Hoc Analysis:
Advanced Excel skills, including the ability to quickly turn around ad hoc data requests, build clear analyses, and summarize results for business partners such as Asset Management and Operations.

Programming:
Strong Python and SQL skills for building models and analyzing data, with hands-on experience using common libraries (e.g., pandas, scikit-learn).Artificial Intelligence:
Baseline experience working with AI tools, including an understanding of prompts and prompt engineering to improve analytical efficiency.

Model Deployment:
Exposure to how models are deployed to production and monitored over time, with willingness to develop these skills alongside team members.

Collaboration:

Ability to work effectively across data science, engineering, and business teams, building strong partnerships and contributing to shared goals.

Communication:
Ability to clearly communicate complex analytical concepts to technical and non-technical audiences.

Essential Job Functions:

Typical Day

Activities:

Partner with business teams, including Asset Management and Operations, to handle ad hoc data…