Position Summary:The Statistician applies advanced statistical and mathematical techniques to analyze real estate data, build predictive models, and deliver actionable business insights. Key duties include designing empirical studies, forecasting demand, optimizing pricing, and ensuring statistical rigor. This hands-on role also writes scalable, production-ready analytical code using software engineering best practices like version control, testing, and code reviews.
Key Responsibilities:- Perform statistical inference and hypothesis testing on real estate datasets to identify significant differences among property and market groups, such as size, location, rent, and demand, and use the results to inform business strategy.
- Develop and validate statistical models using Python or Structured Query Language (SQL)-such as regression, classification, clustering, and time series-to forecast lease rates, predict tenant behavior, and segment markets.
- Maintain rigorous statistical standards and best software engineering practices, including version control, unit testing, documentation, and peer code reviews.
- Design statistically rigorous methods (such as A/B testing, sampling, and experimental design) that generalize to new data.
- Clean, standardize, and evaluate the reliability of raw real estate data (including rent rolls and sensor data) in collaboration with data engineers.
- Maintain production models by monitoring statistical validity, testing for drift, and re-estimating when data changes (while data engineers manage deployment and pipelines).
- Report results through clear, accurate visualizations and dashboards, and explain statistical methods and findings to technical and non-technical audiences to support informed decisions.
- Advise data engineers on the data structures and fields needed to support statistical analysis, and document methods, findings, and tools.
Additional Skills:- Strong foundation in statistical theory, predictive modeling, validation, experimental design, and time-series methods.
- Advanced skills in Python (SciPy, statsmodels, NumPy, pandas, scikit-learn) and SQL for cloud platforms like Snowflake and BigQuery.
- Proficient in Git version control, automated testing, code reviews, containerized environments, and monitoring model drift.
- Strong verbal and written communication; capable of explaining complex statistical findings to both technical and non-technical stakeholders.
Preferred Education:- Bachelor's Degree (or equivalent experience)
Preferred Years of Relevant Work Experience: Compensation:Base Pay Range: $97,500.00 - $128,700.00
Actual placement within this range may vary based upon, but not limited to, relevant experience, time in role, base salary of internal peers, prior performance, business sector, and geographic location.
The Company also offers competitive benefits for full time employees including paid time off, matching 401(k), and health benefits.
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