Senior Data Scientist + Machine Learning Engineer

Still interested? Read on for more information!

Series B preparation underway: You'd be joining at a pivotal stage where early employees have meaningful impact on the company's trajectory.

Real traction: Multiple profitable months and 4x revenue growth year-over-year. Our Q1 was the most successful quarter in the company’s history! This isn't a speculative bet.

Big Customers: Adobe, Brex, CapitalOne, Mercury, Notion, Thomson Reuters, and Whoop, to name a few.

Small team, big ownership: You will own meaningful parts of the ML system that directly impact customers and revenue.

Greenfield problems: Model diverse customer data, scale pipelines, and automate an industry that's barely been touched by software.

Ownership-oriented: You want autonomy and responsibility. You're not looking for someone to hand you a detailed spec and check your work.

Proactive communicator: You identify and raise risks early, summarize what you've heard, and ask clarifying questions rather than making assumptions.

Pragmatic over idealistic: You evaluate solutions based on trade-offs, not dogma.

Product-minded: You care about shipping improvements that move customer outcomes, not just training models.

Comfortable with ambiguity: You can dive into unfamiliar data and systems and figure out what needs to happen.

The data science + ML engineering team consists of four full-time team members (including you) and one part-time employee. You’ll work closely together and collaborate daily with product and engineering to ship new features.

We're early adopters of AI tooling. Everyone on the team uses Claude Code or OpenAI Codex daily (including in sales and GTM), and we actively experiment with new AI workflows. We're looking for someone who sees AI as a force multiplier for skilled practitioners, not a replacement for fundamentals.

Typical day 9:00am — You start work. Check your email and Slack. Review notes from yesterday where you left off. 9:15am — You post an asynchronous update in Slack discussing the Linear tickets you worked on yesterday and what you plan to focus on today. You highlight progress and blockers. You raise a risk related to the estimated timeline of your current project. You at-mention your counterpart on the Engineering team to notify them of a change you’re planning to make to a particular model in the prediction-service and ask if they’d like to discuss further. 9:45am — Join weekly Data Science Tea Time to discuss technical topics relevant to the whole team. 10:45am — Review two pull requests from your teammates. 12:00pm — Lunch break. 1:00PM — Meet with Firas, the CTO who’s also your manager, for your bi-weekly 1:1. Ask some questions about the quarterly goals and new enterprise customers we’re onboarding. Provide him with feedback on a new evaluation methodology we just implemented for a class of AI models in production. 1:30pm — Work on your assigned project. Start a Slack huddle after 30 minutes of investigation to discuss some unfamiliar code with another data scientist. 5:00pm — Take some notes on where you should pick up tomorrow. End the day. If you’re starting a new project soon, you’d participate in a kick-off meeting with all the appropriate stakeholders to ensure you understand the requirements and that the project spec has all the necessary information before you start implementing so that you can deliver what’s expected.

9:00am — You start work. Check your email and Slack. Review notes from yesterday where you left off.

9:15am — You post an asynchronous update in Slack discussing the Linear tickets you worked on yesterday and what you plan to focus on today. You highlight progress and blockers. You raise a risk related to the estimated timeline of your current project. You at-mention your counterpart on the Engineering team to notify them of a change you’re planning to make to a particular model in the prediction-service and ask if they’d like to discuss further.

9:45am — Join weekly Data Science Tea Time to discuss technical topics relevant to the whole team.

10:45am — Review two pull requests from your teammates.

12:00pm — Lunch break.

1:00PM — Meet with Firas, the CTO who’s also your manager, for your bi-weekly 1:1. Ask some questions about the quarterly goals and new enterprise customers we’re onboarding. Provide him with feedback on a new evaluation methodology we just implemented for a class of AI models in production.

1:30pm — Work on your assigned project. Start a Slack huddle after 30 minutes of investigation to discuss some unfamiliar code with another data scientist.

5:00pm — Take some notes on where you should pick up tomorrow. End the day.

If you’re starting a new project soon, you’d participate in a kick-off meeting with all the appropriate stakeholders to ensure you understand the requirements and that the project spec has all the necessary information before you start implementing so that you can deliver what’s expected.

You understand the fundamentals of our technology stack and how ML fits into the product end to end.

You understand our business domain (R&D tax credits and software capitalization), customer workflows, and what “good” looks like.

You’ve shipped multiple measurable quality improvements to production models/pipelines, with monitoring in place.

You’ve established or improved evaluation infrastructure (datasets, metrics, dashboards) that makes iteration faster.

People who prefer research-only roles and do not want to ship production systems.

People who avoid ambiguity and require complete, detailed specs before making progress.

People who cannot evaluate solutions based on trade-offs and real-world constraints.

People who rely on AI as a substitute for core statistical/ML fundamentals.