Data Scientist

Brookfield Properties•Charleston, SC

6d•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 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.

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.