Senior Data Scientist

- 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.