Why This Job is Featured on The SaaS Jobs
This Data Scientist role stands out in SaaS because it sits at the intersection of productized data, customer onboarding workflows, and trust. In subscription businesses, identity verification and risk decisions are not back-office tasks; they directly shape conversion, retention, and the ability to serve regulated customers at scale. The listing signals work on production-grade fraud and compliance systems where model outputs become part of the customer experience.
From a SaaS career perspective, the role builds durable strengths in applied machine learning under real operating constraints. Handling adversarial behavior, sparse signals, and limited ground truth mirrors the realities of many SaaS domains where feedback loops are imperfect and incentives are misaligned. Experience with graph approaches, entity resolution, and labeling strategy also transfers well to other SaaS problems like account hierarchies, usage analytics, and platform integrity.
This position is best suited to someone who prefers pragmatic engineering over research novelty and wants ownership over systems that ship and endure. It will fit professionals who enjoy partnering closely with product and engineering to turn messy data into reliable decisions, and who are comfortable measuring impact through operational outcomes rather than offline metrics alone.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
Employment Type
Full time
Compensation
Middesk is committed to equitable and competitive compensation, including equity and benefits.
This salary range may be inclusive of several career levels at Middesk and will be narrowed during the interview process.
Actual compensation will depend on numerous factors such as: experience, knowledge and skills, qualifications, location, and other job-related factors.
About Middesk
Middesk makes it easier for businesses to work together. Since 2018, we’ve been transforming business identity verification, replacing slow, manual processes with seamless access to complete, up-to-date data. Our platform helps companies across industries confidently verify business identities, onboard customers faster, and reduce risk at every stage of the customer lifecycle.
Middesk came out of Y Combinator, is backed by Sequoia Capital and Accel Partners, and was recently named to Forbes Fintech 50 List.
The Role
We’re building AI-driven applications that simplify customer workflows, starting with business onboarding. With our proprietary identity data and deep domain expertise, we’re in a strong position to expand into a broader set of intelligent, risk-aware products.
We’re looking for a hands-on engineer to help build the foundation for these systems. This role is less about inventing new ML algorithms and more about applying the right techniques to messy, real-world problems. You’ve worked in fraud, risk, or trust domains, and you understand how bad actors behave, how data breaks, and how to still ship reliable systems anyway.
This is a highly technical, hands-on role with broad influence over how we design, build, and scale data-driven systems at Middesk.
What You’ll Do
Build fraud & risk systems
Design and ship production systems that detect and prevent fraud across KYB, trust & safety, and compliance workflows.
Work with messy, real-world data
Tackle problems with extreme class imbalance, sparse signals, evolving adversarial behavior, and limited ground truth.
Leverage relationships in data
Apply graph-based approaches and entity resolution techniques to uncover hidden connections and improve risk detection.
Improve signal & labeling
Use a mix of heuristics, weak supervision, and modern AI tools (including LLMs where appropriate) to generate better features and labels.
Help scale our infrastructure
Partner with engineering to build and evolve systems for feature generation, model training, and production deployment across multiple use cases.
What We’re Looking For
4+ years of experience in fraud, risk, or trust & safety
You’ve worked on real-world fraud or abuse problems and understand the domain deeply.
Experience building and shipping production systems
You’ve deployed models or data-driven systems that power external-facing products.
Strong foundation in applied ML or data systems
Comfortable working on classification problems with real-world constraints like imbalanced data, sparse signals, and changing patterns.
Experience with graph or relational data approaches
Familiarity with knowledge graphs, network analysis, or entity linking is strongly preferred.
Hands-on and pragmatic
You focus on impact over perfection and know how to balance speed, accuracy, and maintainability.
Compensation Range: $155K - $180K
