Findigs, Inc.

Product Designer

• Own the design of product features and surfaces from discovery and research through shipped product, including renter-facing application flows, property manager UI, and internal tools
• Partner directly with product managers and engineers in sprint planning, grooming, and design reviews
• Contribute to the Findigs design system (components, tokens, documentation) and keep it current and usable by both designers and engineers as the product evolves
• Run and support user research: moderated testing, usability sessions, survey design, and synthesis
• Use AI-assisted design tools (Figma AI, AI-assisted prototyping, research synthesis tools) as part of your standard workflow
• Communicate design work clearly in async formats (written rationale, Loom walkthroughs, Figma annotations, and sprint update posts) so the broader team stays informed without needing a meeting
• Contribute your perspective as the design voice in cross-functional settings

Lead Product Designer

Data Scientist

• DecisionAssist model development: Own feature engineering, model iteration, and evaluation for DecisionAssist. You will work across two surfaces: (1) operational model work in the DA/CAV1 serving layer, and (2) analytics-focused modeling in Snowflake for experimentation and research, as well as partner with Product and Engineering on what signals matter and why.
• Experimentation and A/B testing: Design and analyze experiments across underwriting, renter-facing, and PMC-facing product changes, and bring statistical rigor and clear recommendations.
• Predictive and risk modeling: Build and maintain models used in screening logic (e.g., delinquency risk, income estimation, fraud signals).
• ML infrastructure: While you won’t own the warehouse or pipeline architecture, you should be comfortable writing clean Python, working in dbt, and operating in a modern data stack.
• Research and analysis: Tackle high-impact, ad-hoc questions from Product and Customer teams; e.g., what’s driving approval-rate variance, which cohorts behave differently, and what a given signal actually predicts.