Data Scientist / Onsite / Irvine, CA / ML

Job Description We’re hiring a full-time Data Scientist, Personalization & Insights to join our in-office team in Irvine, CA. This role supports an innovative financial rewards platform that transforms rent payments into meaningful financial opportunities for renters across the U.S. Through cashback rewards, flexible payment options, and personalized insights, the platform helps reduce financial stress while creating value for both property managers and advertisers. The mission is bold: to empower renters by turning rent, a typically rigid and unrewarding expense, into a source of financial strength. The platform operates at the intersection of fintech, prop-tech, and ad-tech, creating a connected ecosystem of value and innovation. In this role, you’ll design and deploy machine learning solutions that power personalization, recommendation systems, and actionable insights for renters. You’ll collaborate closely with engineering, product, design, and marketing teams to deliver impactful, responsible AI solutions. This position is ideal for a highly technical data scientist who thrives in a fast-paced startup setting and is eager to make a direct impact. Required Skills & Experience • MS or PhD in Computer Science, Statistics, or a related quantitative discipline • 5+ years of experience building and deploying machine learning systems at scale (e.g., recommender systems, predictive modeling, or ad-tech) • Proven track record of deploying ML models from prototype to production with measurable business results • Expertise in Python (Pandas, PyTorch/TensorFlow/JAX, scikit-learn) • Strong SQL proficiency; experience with big data tools (Snowflake, Spark, BigQuery) • Hands-on experience with GenAI/LLM workflows (prompt engineering, fine-tuning, vector DBs, RAG pipelines) • Familiarity with cloud ML Ops (AWS/GCP/Azure), container orchestration (Docker/Kubernetes), workflow automation (Airflow/Kubeflow) • Experience building and exposing ML services via scalable APIs (REST, GraphQL, gRPC) • Understanding of consumer privacy regulations (GDPR, CCPA/CPRA) • Strong communication skills and product intuition What You Will Be Doing As a Data Scientist, you’ll: • Design, train, and deploy personalization and recommendation models that drive in-app experiences • Build generative AI solutions to personalize copy, creative, and conversations • Develop segmentation and predictive models to uncover actionable renter insights • Build ML Ops infrastructure: feature stores, CI/CD pipelines, A/B testing, model monitoring • Define success metrics and measure the impact of ML features • Expose low-latency model inference services via APIs • Translate product strategy into a data science roadmap and project execution • Conduct experimentation to continuously improve engagement and performance • Contribute to the future of responsible AI at Fancy through mentorship and governance • Collaborate across design, engineering, and marketing to shape data-informed experiences Desired Skills & Experience • Experience in prop-tech, fintech rewards, or credit-building tools • Background in ad-tech, attribution modeling, or two-sided marketplaces • Knowledge of causal inference and A/B testing frameworks • Passion for building consumer-facing products with measurable value Tech Breakdown • 40%: Model design, training, and production deployment • 25%: Infrastructure and ML Ops • 20%: Cross-functional collaboration • 15%: Research, experimentation, and mentorship Daily Responsibilities • Drive end-to-end development of ML-driven personalization systems • Build tooling and infrastructure to support rapid model iteration • Translate user behavior into measurable signals and actions • Ensure privacy, fairness, and explainability in AI systems • Balance fast delivery with long-term technical excellenceBenefits Include • 100% employer-paid medical, dental, and vision for you and your dependents • Equity opportunity • Mission-driven, startup culture that values ownership, curiosity, and authenticity Applicants must be authorized to work in the U.S. now and in the future, on a full-time basis.