Opendoor
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Location
This role requires working from our downtown Toronto office 4 days/week (Mon, Tues, Thurs, Fri). Candidates must be based within a 50-mile commuting distance of the office.
About the Role
At Opendoor, AI isn’t a side project - it’s how we work. Across the company, teams default to AI to solve problems, ship faster, and remove friction from everything we do. You will help define the future of how we work by building AI powered workflows, automation, and experiences that drive leverage across Finance — spanning month-end close, SEC Reporting, FP&A, Technical Accounting, Tax, Procurement, Accounts Payable, and Payroll. This is high agency work in high ambiguity - turning messy problems into clean solutions, prototyping fast, and shipping.
You'll make systems design and tooling decisions that affect how our Finance operations scale – so we need people who think critically about what and why they build, the impact it will have on our team, and how to measure success.
We're looking for AI-native builders who know how to automate complex operational workflows. The accounting and finance domain knowledge can be learned — what matters most is that you're wired to build, that you default to AI in how you approach problems, and that you can map messy business processes into clean, automated systems.
Why Join Us
- Build AI automation for our Financial Operations from the ground up
- Work on cutting-edge systems directly impacting key Finance processes in a public company
- Collaborate with talented engineers and finance team members
- Access to enterprise AI tools — Claude Code, Cursor, and LLM APIs. We invest heavily in AI infrastructure so you can build and experiment.
What You'll Do
This is a hands-on individual contributor role. You will design, build, and ship automations — not just evaluate tools or write strategy decks.
- Turn complex Accounting and Finance problems into clear system designs, experiments, and implementation plans
- Build and maintain AI-powered automation that improves operational efficiency, accuracy, and scale across Finance functions
- Automate workflows (journal entries, reconciliations, flux analysis) using SQL, Python, and agentic AI
- Maintain clean, auditable data throughout automated workflows, with dashboards and controls that support both operational and compliance needs
- Partner with the internal SOX team and external auditors to ensure automated processes meet compliance standards and are well-documented
- Build and scale AI literacy across the Finance team through runbooks, documentation, best practices, and hands-on training
What You'll Bring
- At least 5+ years of work experience building automation, operational workflows, or data systems in any industry.
- Familiarity with LLM deployment, context engineering and comfortable writing SQL or Python code
- Understanding of machine learning lifecycle concepts: training, evaluation, deployment, monitoring
Systems Thinking: You understand how data flows through operational systems. You can look at a business process, identify the inputs, outputs, dependencies, and failure modes, and design automation around it.
System Design: You take complex, messy problems and reduce them to simple, durable architecture.
Communication: Articulate problems clearly to both technical and non-technical audiences. You write clean documentation, build dashboards that tell a story, and can walk a stakeholder through your design rationale.
AI Alchemy: You have high agency and a tinkering mindset. You default to experimenting, running variations with new tools and methods - LLMs, automation platforms, coding assistants until you can transmute data into high quality results. You're comfortable writing SQL code, working with APIs, and stitching tools together to ship solutions fast.
Impact Driven: You optimize for impact over novelty, choosing the right tools for the job and ensure performance, scalability and resiliency.
Bonus if you have:
- Experience in Accounting or Finance
- Comfort working in regulated or compliance-sensitive environments
Tools and Technologies
You'll work across a range of tools and we expect this list to evolve. Ideal candidates will have experience with the following tech stack:
- AI coding assistants: Claude Code, Cursor, or similar
- Workflow automation: Gumloop or similar platforms
- MCP integrations
- Agentic AI frameworks or building automation flows
- Data / ERP platforms: Snowflake, NetSuite, Ramp or similar
Note: Opendoor provides enterprise-level access to all AI tools listed above. Our engineers currently consume billions of AI tokens per month across the company and are not constrained by token limits or personal account restrictions.
Compensation
Our compensation plan consists of a base salary and Opendoor equity in addition to a comprehensive package of benefits including paid time off, paid holidays, medical/dental/vision insurance, basic life insurance, and 401(k) to eligible employees. All compensation parameters are based on experience.
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About the Role
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.
What You'll Need
- Experience developing quantitative models to support real-world decision-making under uncertainty
- Strong 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
Nice to Have
• 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 You'll Do
• 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
Compensation
Compensation
The base pay range for this position is $156,800-$335,000 annually, plus RSUs. Pay within this range varies by work location and may also depend on your qualifications, job-related knowledge, skills, and experience. We also offer a comprehensive package of benefits including unlimited PTO, medical/dental/vision insurance, life insurance, and 401(k) to eligible employees.
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Applied Scientist - ML/Ai
About the Role
We’re looking for an Applied Scientist (ALL LEVELS) to push the boundaries of applied machine learning and AI at Opendoor. While this role will have a significant impact on our valuation systems — ensuring we provide the most accurate and transparent pricing possible — the scope goes well beyond pricing. You’ll work across a range of challenging ML problems, from multi-modal modeling to operational optimization, helping us rethink how we use structured and unstructured data to make better decisions for our customers.
What You'll Need
- Strong software engineering and coding skills in Python, with experience contributing to production codebases
- Experience developing and deploying ML models end-to-end — from research and prototyping to implementation in production systems
- Hands-on experience with deep learning architectures, including ConvNets, Transformers, or similar
- Advanced degree (MS or PhD) in computer science, statistics, mathematics, or a related quantitative field
- Solid foundation in statistics and experimental design
- Strong communication and collaboration skills — you’re comfortable working with cross-functional stakeholders and can communicate technical ideas clearly
Nice to Have
- Familiarity with Pyspark and distributed data processing
- Background in search, recommendation systems, or personalization
- Experience working with large language models (LLMs) or vision-language models (VLMs)
- A genuine interest in real estate — no prior experience required, but you'll engage deeply with housing data
What You'll Do
- Design and deploy architectural improvements to our deep neural network (DNN)-based home valuation models
- Build interpretable ML models that can help us explain pricing decisions to customers
- Incorporate unstructured data — like images, videos, or text — into our forecasting and valuation pipelines using cutting-edge AI models (LLMs, VLMs, etc.)
- Collaborate with Engineering and Ops to enhance our human-in-the-loop pricing systems
- Improve the feature engineering and model training pipelines that power our production systems
- Rethink our risk and optimization models using real-world data and domain insight
We’re a small, nimble team — there’s ample opportunity to work across the entire research and modeling stack.
Compensation
The base pay range for this position is $156,800-$310,000 annually, plus RSUs. Pay within this range varies by work location and may also depend on your qualifications, job-related knowledge, skills, and experience. We also offer a comprehensive package of benefits including unlimited PTO, medical/dental/vision insurance, life insurance, and 401(k) to eligible employees.
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About the Role
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.
What You'll Need
- Experience developing quantitative models to support real-world decision-making under uncertainty
- Strong 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
Nice to Have
• 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 You'll Do
• 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
Compensation
Ready to apply?
Apply to Opendoor
About the Role
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.
What You'll Need
- Experience developing quantitative models to support real-world decision-making under uncertainty
- Strong 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
Nice to Have
• 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 You'll Do
• 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
Compensation
Ready to apply?
Apply to Opendoor
About the Role
We are seeking an exceptional Senior Data Scientist to join our centralized Data organization. In this role, you will be embedded within our consumer and seller experience groups, working closely with cross-functional partners across product, engineering, design, and leadership. You will leverage data to generate deep insights that drive product strategy and create a world-class experience for our users. You'll work with terabytes of data to understand user behavior and directly impact customer satisfaction and business growth.
What You'll Do
- Develop and implement advanced analytics to drive product strategy and enhance the consumer and seller experience.
- Conduct deep-dive statistical analyses on large datasets to uncover user behavior insights and identify key business opportunities.
- Partner with product, engineering, and operations teams to identify opportunities to improve the user journey through data-driven insights.
- Design and execute A/B tests and other experiment types, like multi-armed bandit tests, to measure the impact of product changes and optimize the user experience.
- Build scalable data pipelines and reporting dashboards to deliver insights to stakeholders.
- Present findings and strategic recommendations to executive leadership.
- Contribute to the team and company knowledge base.
What You'll Need
- Advanced proficiency in SQL for complex data manipulation and analysis.
- Strong skills in Python for data analysis and visualization.
- Deep understanding of statistics, A/B testing, and experimental design.
- Excellent communication skills with the ability to present to executive leadership.
- Strong business intuition and the ability to translate technical findings into business recommendations.
- A self-motivated individual with a desire to solve complex, open-ended problems.
Education & Experience
Bachelor's degree or higher in a quantitative field such as Statistics, Economics, Mathematics, or Computer Science.
8+ years of experience in data science, product analytics, or a similar quantitative role.
3+ years of experience in e-commerce, two-sided marketplaces, or consumer-facing products preferred.
What We Offer
- Executive-level visibility with a direct impact on core business metrics.
- Opportunity to work on high-impact analytics projects that shape product strategy.
- Hybrid work model: 4 days in office, 1 day remote flexibility.
- Collaborative culture with cross-functional team integration.
- A fast-paced, high-growth environment with significant learning opportunities
Compensation
Our compensation plan consists of a base salary, Opendoor equity and a comprehensive package of benefits including paid time off, paid holidays, medical/dental/vision insurance, basic life insurance, and 401(k) to eligible employees. All compensation parameters are based on experience.
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Data Scientist, Pricing
Location: In office 4 days a week; Ontario, Canada.
Opendoor is transforming one of the largest, most complex markets in the world — residential real estate — using data at massive scale. Every pricing signal we generate directly impacts how we value homes, how we manage risk, and how efficiently capital moves through our marketplace. The work is highly leveraged: the quality of our pricing decisions influences conversion, margins, customer trust, and the company’s financial performance.
We are looking for mid to senior level Data Scientists. In this role, you will be a core driver of how Opendoor prices real estate at scale. You’ll operate at the intersection of economics, machine learning, experimentation, and product strategy — tackling ambiguity, shaping the pricing roadmap, and building models/analyses that materially move the business. Your insights will influence how we evaluate millions of dollars of housing inventory — and directly shape outcomes for our customers, our balance sheet, and the health of our marketplace.
What You’ll Do
- Build and maintain pricing metrics, dashboards, and frameworks.
- Run experiments and causal analyses to measure impact and drive decisions.
- Develop predictive + statistical models that improve pricing accuracy.
- Partner closely with Product, Engineering, and Operations teams to influence roadmap and model deployment.
- Deliver insights and narratives that inform executive strategy.
Skills & Qualifications
- Deep statistical reasoning: hypothesis design, experimental design, causal inference, and ability to distinguish signal vs noise.
- Proven end-to-end ML ownership: data acquisition, feature engineering, model development, validation, deployment, and ongoing monitoring.
- Strong SQL + Python proficiency; comfortable working with production data pipelines and modern ML tooling (e.g., Spark, Airflow, Ray, SageMaker, Vertex, etc.).
- Demonstrated ability to translate complex analytical findings into clear business recommendations and influence cross-functional decision-making.
- Experience working with ill-defined problems and driving clarity on problem definition, success metrics, and realistic tradeoffs.
- High data-quality bar: disciplined approach to validation, bias analysis, and making decisions rooted in evidence vs intuition.
- Effective communicator — able to tell the story behind the model to both highly technical and non-technical audiences.
- 8+ years of experience in data science, product analytics, or a similar quantitative role.
Our compensation plan consists of a base salary, Opendoor equity and a comprehensive package of benefits including paid time off, paid holidays, medical/dental/vision insurance, basic life insurance, and 401(k) to eligible employees. All compensation parameters are based on experience.
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Data Scientist, Pricing
Location: Hyrbrid 4 onsite, 1 remote Seattle WA.
Opendoor is transforming one of the largest, most complex markets in the world — residential real estate — using data at massive scale. Every pricing signal we generate directly impacts how we value homes, how we manage risk, and how efficiently capital moves through our marketplace. The work is highly leveraged: the quality of our pricing decisions influences conversion, margins, customer trust, and the company’s financial performance.
We are looking for mid to senior level Data Scientists. In this role, you will be a core driver of how Opendoor prices real estate at scale. You’ll operate at the intersection of economics, machine learning, experimentation, and product strategy — tackling ambiguity, shaping the pricing roadmap, and building models/analyses that materially move the business. Your insights will influence how we evaluate millions of dollars of housing inventory — and directly shape outcomes for our customers, our balance sheet, and the health of our marketplace.
What You’ll Do
- Build and maintain pricing metrics, dashboards, and frameworks.
- Run experiments and causal analyses to measure impact and drive decisions.
- Develop predictive + statistical models that improve pricing accuracy.
- Partner closely with Product, Engineering, and Operations teams to influence roadmap and model deployment.
- Deliver insights and narratives that inform executive strategy.
Skills & Qualifications
- Deep statistical reasoning: hypothesis design, experimental design, causal inference, and ability to distinguish signal vs noise.
- Proven end-to-end ML ownership: data acquisition, feature engineering, model development, validation, deployment, and ongoing monitoring.
- Strong SQL + Python proficiency; comfortable working with production data pipelines and modern ML tooling (e.g., Spark, Airflow, Ray, SageMaker, Vertex, etc.).
- Demonstrated ability to translate complex analytical findings into clear business recommendations and influence cross-functional decision-making.
- Experience working with ill-defined problems and driving clarity on problem definition, success metrics, and realistic tradeoffs.
- High data-quality bar: disciplined approach to validation, bias analysis, and making decisions rooted in evidence vs intuition.
- Effective communicator — able to tell the story behind the model to both highly technical and non-technical audiences.
- 8+ years of experience in data science, product analytics, or a similar quantitative role.
Base salary range for this role varies. Generally, the base salary range is $186,000 – $256,000 annually + RSUs + ESPP + additional employee benefits (medical/dental/vision, life insurance, unlimited PTO, 401K).
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Location
This role requires working from the downtown Toronto office 4 days/week (Mon, Tues, Thurs, Fri). Candidates must be based within a 50-mile commuting distance of the office.
About the Role
As a Finance Automation Lead at Opendoor, you will join the Finance team reporting to the President of Opendoor with a mandate to 10x our data leverage across Finance. This is a high-impact IC role at the intersection of data architecture, AI engineering, and financial systems.
Opendoor has adopted a "Default to AI" philosophy—if AI can handle it, AI should handle it. We're not theorizing about AI transformation; we're shipping it. You are a builder with a product mindset who will help transform Finance from a service function into a product—one that delivers real-time insights, automated workflows, and self-service capabilities to every team at Opendoor. You understand how LLMs work, you’ve built agents, and you understand how finance data can be leveraged and structured with these models.
Build AI-Powered Finance Infrastructure
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Design and deploy AI agents that automate data fetching, analysis, reporting, and forecasting workflows
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Architect data pipelines connecting financial planning systems (Adaptive) to Snowflake and downstream applications
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Build semantic layers that enable non-technical finance team members to access data through natural language
Create Finance Tools & Products
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Develop internal tools that transform manual finance processes into automated, scalable products
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Build dashboards and self-service interfaces that eliminate analyst bottlenecks
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Create systems that enable managers to answer their own headcount, budget, and forecast questions without finance intervention
Own Data Architecture for Finance
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Organize finance data for best-in-class LLM capabilities
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Design data models that support automated analysis, real-time querying, and AI agent interactions
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Establish data quality frameworks and governance for financial systems
Drive Automation at Scale
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Identify high-value automation opportunities across Finance (accruals, reporting, forecasting, variance analysis)
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Implement workflow automation using tools like Gumloop, Claude, and custom-built agents
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Measure and improve automation accuracy, reducing manual intervention over time
What You Will Bring
Technical Foundation
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At least 3+ years of experience building data systems in a finance, analytics, or data engineering role
- Track record of building and deploying AI agents or LLM-powered applications in production environments
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Advanced SQL proficiency with ability to design complex data models and optimize query performance
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Production experience with Python for data engineering, automation, and AI agent development
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Hands-on experience with modern data warehouses (Snowflake, BigQuery, or similar)
AI & Automation Expertise
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Deep understanding of LLM capabilities, limitations, and prompt engineering best practices
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Experience architecting semantic layers or natural language interfaces for data access
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Familiarity with AI orchestration frameworks (LangChain, LlamaIndex, or similar)
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Proven ability to identify high-ROI automation opportunities and implement end-to-end solutions
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Experience with workflow automation tools and API integrations
Data Architecture & Systems Design
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Strong data modeling skills with focus on enabling both human analysts and AI agents
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Experience designing data pipelines that connect planning systems (like Adaptive, Anaplan) to data warehouses
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Understanding of data governance, quality frameworks, and metadata management
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Ability to architect scalable systems that reduce analyst bottlenecks and enable self-service
Product & Business Mindset
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Builder mentality with ability to ship functional products, not just analyses
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Experience translating messy business problems into clean technical solutions
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Strong stakeholder management skills with ability to influence senior finance leadership
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Comfort working in ambiguous, fast-moving environments with evolving requirements
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Systems thinking approach to understanding complex problems and their root causes
Bonus points if you have:
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Experience in fintech, real estate tech, or high-velocity transaction businesses
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Background in financial planning & analysis (FP&A) or corporate finance
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Familiarity with finance-specific tools (Adaptive Insights, NetSuite, etc.)
Compensation
Our compensation plan consists of a base salary, Opendoor equity and a comprehensive package of benefits including paid time off, paid holidays, medical/dental/vision insurance, basic life insurance, and 401(k) to eligible employees. All compensation parameters are based on experience.
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Ready to apply?
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