Data Scientist - job 1 of 2

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 and cited as an industry leader in business verification by digital identity strategy firm, Liminal.

The Role:

We are looking for an individual to help build 0-to-1 machine learning-powered products and scale the data science operations that support the growth of our core data offerings in the fraud, risk, and identity verification space. As one of the founding Data Scientists, you will have the opportunity to both build and lead.

On a day-to-day basis, you will work closely with internal stakeholders – including go-to-market, data platform, and product engineering teams – as well as external clients on some of our most impactful and visible projects. Your primary focus will be to build and scale the analytics function that supports ML-powered products and to craft performance and ROI narratives that demonstrate the value of our data insights.

A secondary focus of the role involves contributing to the development of our ML products. You’ll help define how we evaluate, monitor, and operationalize data science initiatives that support our product roadmap and customer needs.

While the role includes some reporting work — such as building evaluation tables and maintaining related ETL pipelines — it is not reporting-heavy.

Above all, success in this role depends on your ability to go beyond solving isolated problems. You will be expected to design scalable frameworks and systems that generalize across future requests, using automation and emerging technologies like GenAI to create leverage over time.

We follow a hybrid work model, and for this role, there is an expectation of 2 days per week in our SF or NY office. Candidates should be based within a commutable distance, as we believe in the value of in-person collaboration and building strong team connections while also supporting flexibility where possible.

What You'll Do:  

• Work with the Product team to build best-in-class business identity, fraud, and risk solutions:

  • Collaborate with Product and Engineering to design and evaluate ML models tied to business identity, fraud, and risk.

  • Lead experimental projects related to new feature engineering, labeling strategies, and data enrichment.

  • Evaluate external data vendors and define our data acquisition strategy to create the most comprehensive Business Identity dataset in the U.S.

• Own and scale the external-facing data science support function:

  • Build model scoring pipelines and tools to help clients evaluate the performance of our ML products.

  • Support strategic customers through deep-dive analysis, risk policy optimization, and performance reporting.

  • Create technical collateral — model usage guides, ROI narratives, and ML/AI governance documents—to enable Sales, Customer Success, and Marketing.

  • Leverage GenAI and automation to triage and scale analytics support for external teams and clients.

• Create the operating model for ML analytics at Middesk:

  • Design and implement a scalable framework for ML-product analytics, model backtesting, and performance monitoring.

  • Build and maintain evaluation tables, ETL pipelines, and DBT jobs to support model tracking and governance.

  • Develop runbooks, documentation, and automation to make analytics workflows reliable and repeatable.

  • Help shape the long-term strategy, tools, and culture of the Data Science function.

What We’re Looking For:

• 3+ years of experience in data science, analytics engineering, or ML analytics, ideally within a B2B SaaS or fintech company.

• Proficiency in SQL (Spark), Python; experience building data workflows with tools like DBT and Airflow.

• Hands-on knowledge of ML evaluation and operations (especially for classification-based models).

• Strong communicator who can translate technical insights into clear narratives for internal and external audiences.

• Demonstrated ability to work cross-functionally and operate with a high degree of ownership.

• A builder’s mindset—comfortable working in ambiguity and focused on scalable, repeatable systems.

Bonus Points:

• Experience in fintech or financial services (fraud, identity verification, KYB/KYC, payments, or lending), ideally from high-growth, venture-backed startups.

• Familiarity with supporting go-to-market and external client teams on data-driven products.