Who you are

- Experience developing quantitative models to support real-world decision-making under uncertainty

- Robust coding skills in Python, with the ability to move beyond prototyping and implement production-quality scientific code

- Experience with one or more of the following: causal inference, Bayesian modeling, structural modeling, demand forecasting, pricing science, or mathematical optimization

- Comfort working with messy, high-dimensional real-world data and translating ambiguous business problems into rigorous modeling approaches

- Advanced degree (MS or PhD preferred) in statistics, mathematics, economics, operations research, computer science, or another quantitative discipline

- Strong communication and collaboration skills — you’re comfortable working with cross-functional stakeholders and can communicate technical ideas clearly

- Experience in pricing, marketplace modeling, revenue management, supply/demand systems, inventory optimization, or risk modeling

- Background in real estate, housing, finance, or adjacent marketplace domains

- Familiarity with distributed data processing tools such as Pyspark

- Experience with machine learning methods broadly, including where deep learning can complement structured statistical modeling

- Experience working with large language models (LLMs) or vision-language models (VLMs)

What the job involves

- We’re looking for an Applied Scientist to work on some of the hardest quantitative problems at Opendoor. This role will focus primarily on structural modeling, econometrics, optimization, and decision-making under uncertainty, with applications spanning pricing, resale strategy, demand modeling, and risk management





- This role will contribute to our broader valuation and pricing ecosystem and we’re looking for someone who can combine strong modeling intuition with hands-on execution and strong engineering to build practical solutions for a low-margin, high-stakes business where small improvements can have an outsized impact

- You’ll work on problems like modeling post-listing demand, estimating price elasticity, designing experiments, building structural models, and developing optimizers that help us make better decisions across our products and inventory

- We’re a small, nimble team, so there’s ample opportunity to shape both the modeling direction and how these systems get used in production decision-making

- Build models that help Opendoor make better decisions around pricing, resale strategy, and portfolio risk

- Develop demand and conversion models using both pre-listing and post-listing signals

- Design and improve optimization frameworks that balance objectives like margin, conversion, and risk

- Apply statistical, econometric, and mathematical modeling techniques to problems where structure matters and pure black-box prediction is not enough

- Design experiments and measurement approaches to quantify price elasticity, customer response, and product trade-offs

- Partner with Engineering, Product, and Operations to turn models into systems that influence real decisions

- Bring a pragmatic, hands-on approach: move quickly from idea to prototype to production-ready scientific component

Benefits

- Save for tomorrow: Select our Pre-tax or Roth 401k plans to save towards retirement

- Healthy healthcare: Medical, dental and vision is 100% covered for employees

- Family planning: We offer specialty programs like Carrot, Cleo & Ginger

- Flexible vacation: Most of our team take at least 20 days off per year to decompress and be their best

- Resource groups: Join the group that works for you

- Income protection: 100% company sponsored: life insurance short term and long term disability

- Other: Flexible spending account, commuter benefit, employee assistance programs, and paid parental leave

📌 Applied Scientist (Toronto)
🏢 Opendoor
📍 Toronto