Data Scientist

Cushman & Wakefield

About The Position

We are building an Advisory Intelligence capability that applies advanced analytics, econometrics, and AI to some of the most complex questions in commercial real estate and investment advisory, market risk, pricing dynamics, liquidity, valuation context, and capital allocation. We are looking for a Junior Data Scientist with a strong quantitative foundation (Master’s level) and experience in analytical problem-solving environments, such as large consulting firms or investment-focused teams. This is a role for someone who enjoys structured thinking, experimentation, and rapid hypothesis testing, and wants to work on problems where data, economics, and judgment intersect. 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. 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.

Requirements

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

Nice To Haves

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)

Responsibilities

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
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
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
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
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