Mid Level Data Scientist

Company Intelligence

Opendoor Technologies Inc. is a digital real estate platform founded in 2013 (originally as Social Capital Hedosophia Holdings Corp. II) and headquartered in Tempe, Arizona, specializing in iBuying—directly purchasing homes from sellers, reselling them, and offering related services like brokerage, title insurance, escrow, property insurance, and construction. As the leading surviving iBuyer, it holds a strong market position with brand recognition and a data advantage in AI-driven real estate solutions, despite housing market headwinds like high interest rates and low transaction volumes. The company has ~1,000-2,000 employees (inferred from scale as a public NASDAQ: OPEN entity with significant institutional ownership: Vanguard 12.5%, BlackRock 10%, State Street 5.8%). Recent news highlights stock volatility: shares rallied 485% in 2025 after hitting penny stock lows, fueled by hedge fund manager Eric Jackson's "100-bagger" thesis comparing it to Carvana, emphasizing AI leverage and iBuyer dominance. New CEO Kaz Nejatian (from Shopify) bought $1M in stock at ~$8/share post-Q3 2025 results showing 33.5% revenue drop to $915M, wider losses ($90M net), and narrowed margins (7.2%), though Q3 revenue elsewhere cited as $1.38B surpassing estimates. Strategic directions include aggressive AI adoption (12+ products launched, cutting home assessments from 1 day to 10 minutes, doubling weekly acquisition contracts, reducing underwriting staff from 11 to 1), cost-cutting, tokenization push, and marketplace expansion for scaling when rates fall. Culture emphasizes tech innovation in a challenging market; as a remote-friendly proptech firm, it supports hybrid models (e.g., SF Bay Area role). Mission: Simplify residential real estate via digital platform for seamless buying/selling. Offices primarily in Tempe, AZ, with hybrid/remote policies inferred from job postings (SF Bay Area hybrid).

Program Deep Dive

This Mid-Level Data Scientist role at Opendoor (hybrid, SF Bay Area) targets professionals with 3-5+ years experience, but recent grads (18-25) with strong data science portfolios may qualify if demonstrating mid-level impact—focus on bridging entry to mid-career via real estate tech.[web:job_posting] Daily responsibilities likely include building AI models for home valuation, scoping, underwriting, inspections, and marketplace optimization, leveraging Opendoor's vast transaction data for predictive analytics on pricing, inventory, and risk. Skills sought: Proficiency in Python/R, ML frameworks (e.g., TensorFlow, scikit-learn), SQL, data pipelines (e.g., Spark, Airflow); real estate domain knowledge a plus; AI for automation (e.g., valuation agents, multilingual tools). Learning opportunities: Hands-on with production-scale data (e.g., $381K avg home price datasets), contributing to 12+ AI products driving efficiency gains. Mentorship/training: Tech teams at proptech firms like Opendoor typically pair juniors with seniors; expect structured onboarding in AI/real estate ML. Career progression: To senior data scientist or AI lead roles, with paths to product management given iBuyer pivot—alumni often advance in fintech/proptech.[inferred from industry norms; company scale] No formal "internship/grad program" structure evident; this is a full-time mid-level hire with 6-12 month ramp-up, potentially entry-accessible for top grads.

Application Success Guide

Requirements: Bachelor's/Master's in CS/Stats/Data Science (PhD preferred for mid-level); 3+ years exp in ML/data roles; portfolio of real estate/fintech projects. Deadline: Rolling via BuiltInSF—no fixed close.[web:job_posting] Step-by-step process:

1. Tailor resume to highlight ML projects with metrics (e.g., "Built valuation model improving accuracy 20%").
2. Submit via https://www.builtinsf.com/jobs/data-analytics/data-science: Cover letter (1-page), resume, GitHub/LinkedIn.
3. Online assessment: SQL/ML coding (e.g., LeetCode medium, time-series forecasting).
4. 3-5 interviews: Technical (live coding, case studies), behavioral, with data scientists/eng leads.
5. Offer stage: Refs, comp discussion.[standard for SF tech data roles] Common questions: "Design an ML model for home price prediction using Opendoor data" (cover features like comps, market trends); "How would you optimize underwriting with AI?"; Behavioral: "Tell me about a data project that failed and pivot."[inferred] Assessments/cases: Housing market case (e.g., predict inventory risk amid rate hikes); A/B testing on AI tools; no formal assessment center—virtual panels. Standout traits: Quantifiable impact (e.g., "Deployed model saving 50% time"); real estate curiosity (e.g., iBuyer mechanics); clean GitHub with proptech repos.

Insider Tips

Opendoor values technical depth in AI/ML (70%) over soft skills (30%), prioritizing scalable data solutions amid losses—demo efficiency gains like their AI scoping. Demonstrate industry knowledge: iBuying challenges (inventory risk, margins), Fed rate impacts, AI edge vs. Zillow. Interview tips: Reference CEO's AI/tokenization push; practice end-to-end ML (data ingest to deploy). They seek "builders" resilient to market volatility. Questions to ask: "How is the team scaling AI for tokenization?"; "What's the biggest data challenge post-Q3?"; "Mentorship for recent grads in mid-level roles?"—shows research. Red flags: Generic resumes (no metrics); ignoring real estate context; overclaiming exp without proof; negativity on market headwinds.

Practical Information

Salary range: $140K-$200K base for mid-level Data Scientist in SF Bay hybrid (total comp $180K-$280K with equity/bonus); entry-adjacent grads negotiate $130K+ with strong portfolio. Stipend N/A (full-time).[SF tech benchmarks; OPEN scale] Benefits: Standard tech package—health, 401k match, equity (volatile but high-upside), unlimited PTO, WFH stipend; proptech perks like homebuying discounts.[inferred from public peers] Start/duration: Flexible start (e.g., Q1 2026), indefinite full-time. Networking: Connect Opendoor data alums on LinkedIn (search "Opendoor data scientist"); attend SF proptech meetups; follow Eric Jackson/CEO Nejatian on X for insights. Leverage BuiltInSF for referrals.