{"id":2920543,"url":"https://alion.io/job/opendoor-applied-scientist-pricing","title":"Applied Scientist- Pricing","company":{"id":2680,"name":"Opendoor","domain":"opendoor.com","url":"https://alion.io/company/opendoor-labs-inc","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Schema","truth_index":{"grade":"A","score":95,"open_postings":37,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":108,"computed_at":"2026-11-04T06:34:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Python","optional":false},{"name":"Machine Learning","optional":true},{"name":"pySpark","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-05-12T02:43:21Z","employer_posted_date":"2026-05-12","last_verified_at":"2026-11-01T12:14:08Z","board_verified":false,"closed_at":null,"days_open":176,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":176},"description":"About Opendoor\nAt Opendoor our mission is to tilt the world in favor of homeowners and those who aim to become one. Homeownership matters. It's how people build wealth, stability, and community. It's how families put down roots, how neighborhoods strengthen, how the future gets built. We're building the modern system of homeownership giving people the freedom to buy and sell on their own terms. We’ve built an end-to-end online experience that has already helped thousands of people and we’re just getting started.\nAbout the Role\nWe’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.\nThis 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.\nYou’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.\nWe’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.\nWhat You'll Need\nExperience developing quantitative models to support real-world decision-making under uncertainty\nStrong coding skills in Python, with the ability to move beyond prototyping and implement production-quality scientific code\nExperience with one or more of the following: causal inference, Bayesian modeling, structural modeling, demand forecasting, pricing science, or mathematical optimization\nComfort working with messy, high-dimensional real-world data and translating ambiguous business problems into rigorous modeling approaches\nAdvanced degree (MS or PhD preferred) in statistics, mathematics, economics, operations research, computer science, or another quantitative discipline\nStrong communication and collaboration skills - you’re comfortable working with cross-functional stakeholders and can communicate technical ideas clearly\nNice to Have\nExperience in pricing, marketplace modeling, revenue management, supply/demand systems, inventory optimization, or risk modeling\nBackground in real estate, housing, finance, or adjacent marketplace domains\nFamiliarity with distributed data processing tools such as Pyspark\nExperience with machine learning methods broadly, including where deep learning can complement structured statistical modeling\nExperience working with large language models (LLMs) or vision-language models (VLMs)\nWhat You'll Do\nBuild models that help Opendoor make better decisions around pricing, resale strategy, and portfolio risk\nDevelop demand and conversion models using both pre-listing and post-listing signals\nDesign and improve optimization frameworks that balance objectives like margin, conversion, and risk\nApply statistical, econometric, and mathematical modeling techniques to problems where structure matters and pure black-box prediction is not enough\nDesign experiments and measurement approaches to quantify price elasticity, customer response, and product trade-offs\nPartner with Engineering, Product, and Operations to turn models into systems that influence real decisions\nBring a 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