Location: Manhattan, NY

Type: Full-time (Onsite – 5 days per week)

Department: Applied AI Research – Forty5Park

About Forty5Park

At Forty5Park, we’re building the next generation of AI systems for real estate — leveraging data science, intelligent automation, and advanced machine learning to uncover new insights and drive smarter decisions across the industry.

Our team of scientists, engineers, and analysts approaches real estate through the lens of quantitative research — blending rigorous experimentation with scalable technology. We believe in using data-driven methods, generative AI, and mathematical modeling to transform how the built environment is understood and optimized.

About the Role

We’re seeking a Machine Learning Researcher with a strong background in forecasting, quantitative modeling, and statistical learning to join our Applied AI team. In this role, you’ll apply a scientific, hypothesis-driven approach to build models that forecast market trends, asset valuations, and other key real estate dynamics.

You’ll work at the intersection of machine learning, econometrics, and applied statistics, exploring innovative modeling techniques — from Bayesian inference and Markov processes to deep learning architectures for time-series forecasting.

As part of the AI research function, you’ll collaborate with data engineering, platform engineering, and domain experts to transform novel research ideas into production-ready models that power real-world decision systems across Forty5Park.

Key Responsibilities

• Design, develop, and test forecasting models for property valuations, price trends, market risk, and other quantitative real estate metrics.
• Apply machine learning, Bayesian inference, and Markovian modeling to large-scale, multidimensional datasets.
• Formulate hypotheses, design experiments, and analyze results using rigorous statistical methods.
• Partner with engineers to prototype, validate, and deploy predictive models into production environments.
• Contribute to the research culture through reading groups, internal seminars, and collaboration on emerging methodologies.
• Stay current with advances in time-series forecasting, causal modeling, and AI research through conferences and academic engagement.