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Job description
4 to 8 years of experience in data science or advanced analytics, with meaningful exposure to commercial real estate, financial services, or similarly complex transactional data environments.
Expert-level SQL in both PostgreSQL and Snowflake, including query optimization, window functions, and complex multi-table joins across large datasets.
Proficiency in Python for data manipulation, statistical modeling, and automation -- pandas, scikit-learn, and similar libraries used in practice, not just on a resume.
Hands-on experience building and evaluating predictive models: regression, classification, time-series forecasting, and anomaly detection applied to real business problems.
Working knowledge of ETL and CDC concepts: understanding how data flows from source systems into a cloud data warehouse and how to trace data quality issues upstream.
Hands-on experience with AWS or another major cloud platform (Azure, GCP), including cloud-hosted data infrastructure, S3, and managed compute services.
Proven ability to work directly with senior leaders -- presenting findings with confidence, educating stakeholders on methodology, and fielding hard questions under pressure.
Proficiency with AI tools including Claude to accelerate analysis, automate repetitive tasks, and improve turnaround on data requests.
Strong written and verbal communication skills: the ability to make model output and statistical findings accessible to non-technical audiences without dumbing them down.
High sense of urgency: able to hit the ground running with minimal ramp-up and deliver from day one.
Required
4 to 8 years of experience in data science or advanced analytics, with meaningful exposure to commercial real estate, financial services, or similarly complex transactional data environments.
Expert-level SQL in both PostgreSQL and Snowflake, including query optimization, window functions, and complex multi-table joins across large datasets.
Proficiency in Python for data manipulation, statistical modeling, and automation -- pandas, scikit-learn, and similar libraries used in practice, not just on a resume.
Hands-on experience building and evaluating predictive models: regression, classification, time-series forecasting, and anomaly detection applied to real business problems.
Working knowledge of ETL and CDC concepts: understanding how data flows from source systems into a cloud data warehouse and how to trace data quality issues upstream.
Hands-on experience with AWS or another major cloud platform (Azure, GCP), including cloud-hosted data infrastructure, S3, and managed compute services.
Proven ability to work directly with senior leaders -- presenting findings with confidence, educating stakeholders on methodology, and fielding hard questions under pressure.
Proficiency with AI tools including Claude to accelerate analysis, automate repetitive tasks, and improve turnaround on data requests.
Strong written and verbal communication skills: the ability to make model output and statistical findings accessible to non-technical audiences without dumbing them down.
High sense of urgency: able to hit the ground running with minimal ramp-up and deliver from day one.
Preferred
Experience with large language model (LLM) integrations, prompt engineering, or RAG pipelines applied to document-heavy analytical workflows.
Familiarity with commercial real estate concepts including lease structures, rent schedules, break clauses, market comparables, and transaction economics.
Experience with BI tools such as Sigma Computing, Tableau, or Power BI for presenting model outputs and analytical dashboards.
Familiarity with data testing frameworks -- dbt tests, Great Expectations, or SODA -- and a habit of building validation into the analytical process, not bolting it on afterward.
Background supporting advisory, research, or transaction services teams within a CRE or financial services organization.
Exposure to vector databases, embedding models, or semantic search applied to document retrieval.
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Worksite address
dallas, TX, 75215, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.