Cushman & Wakefield Global, Inc is looking for a Lead Data Engineer to architect and drive the development of enterprise-scale data platforms and advanced analytics solutions. You will combine deep technical expertise with leadership responsibilities to innovate and establish best practices across multiple business units.

What You'll Do

Partner with business leadership to identify impactful areas for the application of AI and ML.
Lead data science and data-driven analytics engagements across clients in various sectors.
Design and optimize ML models for predictive and prescriptive analytics solutions.
Explore advanced techniques in AI and ML to enhance model performance.
Maintain generative AI models along with development and updating of code and process documentation.
Partner with data stewards to improve data availability and quality while ensuring compliance.
Partner with technology teams to deploy AI and ML solutions in scalable environments.
Use business intelligence tools for better visualization and transparency of data-driven insights.
Manage a team of data engineers, data scientists, and analytics professionals to deliver data-driven insights and solutions.
Track the latest AI innovations and build relevant proofs of concept to further our capabilities.
Participate in organization building, hiring, partnerships, and conferences.

What We're Looking For

Advanced statistics & machine learning expertise (e.g., causal inference, experimentation, forecasting, NLP, recommender systems, or deep learning).
Strong programming skills.
Production-quality code in Python or R.
SQL mastery, including complex queries and performance tuning.
Experience deploying models to production.
Knowledge of model lifecycle, monitoring, retraining, and data drift.
Solid data engineering awareness.
Experience with data pipelines, feature stores, and cloud platforms (Azure/ML flow).

Nice to Have

Experience in Databricks.
Hands-on experience with machine learning pipelines or integrating data engineering with advanced analytics and AI workflows.
Familiarity with data governance frameworks, including data lineage, cataloging, and compliance (e.g., GDPR, HIPAA).
Background in delivering data-driven solutions for commercial real estate applications.
A degree in IT, Engineering, computer sciences, business IT, or any quantitative discipline.