À propos
Data Scientist Job Description Summary
We are building an Advisory Intelligence capability that applies advanced analytics, econometrics, and AI to complex questions in commercial real estate and investment advisory, market risk, pricing dynamics, liquidity, valuation context, and capital allocation. Job Description
This is not a reporting or dashboarding role. The work is exploratory by design: testing ideas, building proof-of-concept models, and experimenting with advanced techniques, including econometrics, machine learning, and emerging AI approaches, to shape how advisory insights are generated and delivered. What You’ll Actually Do
Work on ambiguous, high-impact problems at the intersection of real estate markets, investment behavior, and socio-economic forces Build analytical and AI-driven PoCs that explore new ways to assess market conditions, risk, and opportunity Apply econometric and statistical techniques beyond basic regression, including time-series, panel data, probabilistic, and clustering methods Experiment with machine learning, generative AI, and agentic AI to augment research, analysis, and decision-making Use platforms such as Databricks to explore and model complex datasets in an analytical environment Translate quantitative work into clear insights and implications for senior advisors and leadership Key Responsibilities
Analytical Modeling & Econometrics
Develop and test statistical and econometric models to analyze CRE market behavior, pricing dynamics, risk factors, and investment conditions Apply a range of techniques beyond regression, including: time-series analysis; panel and longitudinal data modeling; probabilistic and distribution-based methods; dimensionality reduction and clustering techniques Evaluate model assumptions, limitations, and sensitivity to changing inputs Support scenario analysis and exploratory stress testing for advisory use cases AI & Advanced Analytics (Experimental Focus)
Build and evaluate machine learning and AI-based PoCs applied to CRE-specific problems (e.g., market condition scoring, liquidity risk, valuation dispersion) Support experimentation with generative AI and large language models (LLMs) for research synthesis, insight generation, and analytical augmentation Contribute to early implementations of agentic AI, including multi-step analytical workflows, tool-using agents, and human-in-the-loop systems Help assess where AI adds decision value versus where traditional statistical approaches are more appropriate Socio-Economic & Market Context Modeling
Incorporate socio-economic, demographic, labor, income, education, and other external indicators into market-level and submarket-level analyses Support spatial and place-based analysis to contextualize asset and market performance Connect macroeconomic and local indicators to CRE outcomes in a structured, explainable way Data Exploration & PoC Enablement
Perform targeted data collection, cleaning, and integration as required for specific PoCs Work with internal and third-party data sources in analytical environments such as Databricks Collaborate with data engineering and platform teams when PoCs move toward scaling Clearly document analytical approaches, assumptions, and findings to support knowledge transfer Advisory & Stakeholder Collaboration
Translate analytical outputs into clear insights, signals, and implications for advisory and investment-focused audiences Collaborate with senior advisors, product leaders, and researchers to refine problem statements and analytical direction Communicate findings in a structured, concise manner appropriate for executive and client-facing contexts Who This Is For
Hold a Master’s degree in Data Science, Mathematics, Econometrics, Statistics, Economics, Engineering, or a closely related quantitative field Have 1–3 years of experience in consulting, investment finance, or CRE advisory/research Enjoy problem-solving and experimentation more than maintaining production pipelines Are comfortable moving between theory and application Have strong proficiency in Python for data analysis and modeling Have solid foundation in statistical modeling and quantitative reasoning Are intellectually curious, structured in your thinking, and comfortable working in uncertain problem spaces Preferred Qualifications
Applied experience with econometric or advanced statistical techniques beyond basic regression Exposure to commercial real estate, investment analysis, or market research workflows Familiarity with Databricks or similar analytical data platforms Experience working with socio-economic or macroeconomic datasets Exposure to machine learning, generative AI, or LLM-based applications Hands-on experience (professional or academic) with agentic AI or autonomous analytical workflows Experience building proofs of concept rather than only production systems Familiarity with data visualization tools (Tableau, Power BI, or Python libraries) What Success Looks Like
High-quality analytical PoCs that help the business evaluate new ideas, signals, and decision frameworks Clear articulation of what was tested, what was learned, and what should happen next Thoughtful application of quantitative methods aligned to advisory and investment questions Increasing independence in analytical exploration and model development Growing contribution to AI-enabled insights and future data products within Advisory Intelligence Note: Cushman & Wakefield is an Equal Opportunity employer. The company provides eligible employees with a comprehensive benefits package and competitive pay, with details aligned to eligibility and location.
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Compétences linguistiques
- English
Avis aux utilisateurs
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