Data Scientist

Maymont Homes•Charleston, SC

7d•Onsite

About The Position

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.

Requirements

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.
Solid working knowledge of statistical modeling, predictive analytics, regression, and core machine learning methods, with hands-on experience building models.
Ability to take a defined business problem, develop an analytical approach, and translate findings into clear, usable recommendations for business partners.
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.
Strong Python and SQL skills for building models and analyzing data, with hands-on experience using common libraries (e.g., pandas, scikit-learn).
Baseline experience working with AI tools, including an understanding of prompts and prompt engineering to improve analytical efficiency.
Exposure to how models are deployed to production and monitored over time, with willingness to develop these skills alongside team members.
Ability to work effectively across data science, engineering, and business teams, building strong partnerships and contributing to shared goals.
Ability to clearly communicate complex analytical concepts to technical and non-technical audiences.

Nice To Haves

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.

Responsibilities

Partner with business teams, including Asset Management and Operations, to handle ad hoc data requests and support day-to-day operational and portfolio questions.
Build predictive models in Python to address defined business problems, such as pricing, occupancy, or operational performance, in collaboration with senior team members.
Help deploy models into production and monitor their performance, learning production best practices with support from senior team members and Data Engineering.
Summarize findings into clear, concise takeaways for business partners.
Collaborate with Data Engineering to ensure scalable, reliable, and trusted analytical datasets.
Provide timely, accurate analysis and ad hoc data support that helps business partners make better decisions.
Build and maintain models in Python that reliably address the business problems they are assigned to solve.
Deliver accurate, well-organized analyses that business partners can trust and use.
Steadily expand technical skills and business knowledge, including new tools and modeling techniques and the housing industry, over time.
Ensure analytical rigor, statistical integrity, reproducibility, and documentation across all models and analyses.