Senior Data Scientist

The Role

We are looking for a hands-on Senior Data Scientist who uses Python and agentic development tools, like Claude Code or similar, to design and implement statistical and machine learning models that directly impact our customers' financial outcomes. You will own the full modeling lifecycle across credit underwriting, risk management, and forecasting, and play a key role in shaping how we use data to serve small businesses better, and contribute to the technical direction of the broader data science teamThere are significant opportunities to grow and lead at Pipe.

Your key responsibilities will include:

• Independently developing and owning statistical models that forecast cash flows and other drivers of business and customer health.
• Designing and building models that optimize our product offerings to better serve our customers' needs.
• Proactively exploring datasets to surface insights, and prototyping new approaches that create measurable value for customers.
• Taking full outcome ownership of deployed models, including performance monitoring, residual analysis, and iteration, in close collaboration with engineering.
• Mentoring junior team members, raising the technical bar through code and design review, and contributing to our data science best practices and long-term tooling roadmap.
• Partnering with engineering and data engineering to productionize models, shape data infrastructure requirements, and improve our MLOps foundations.

Qualifications

We are looking for a motivated, intellectually curious Senior Data Scientist with the following qualifications:

• 4-7+ years of experience developing and deploying machine learning or statistical models in a production environment (inclusive of relevant post-undergraduate academic work)
• Strong proficiency in Python and the broader data science ecosystem (NumPy, Pandas, scikit-learn, etc.), and high proficiency in SQL.
• Familiarity with cloud-based MLOps platforms (e.g., SageMaker, Vertex AI, or similar) and comfort partnering with engineering to deploy and monitor models in production.
• Personal or professional experience with, or strong interest in adopting, agentic development workflows and modern AI-assisted coding tools (e.g., Claude Code, Copilot, or similar).
• Deep fundamentals in probability, statistics, and machine learning, with the ability to choose the right tool for the problem.
• Experience with credit risk modeling, underwriting, or other risk decision science domains such as insurance is strongly preferred.
• Comfortable working cross-functionally and communicating complex modeling decisions clearly to both technical and non-technical stakeholders.
• Strong written and verbal communication skills.
• Bachelor's degree in Computer Science, Financial/Applied Math, Statistics, Economics, or a related technical field. Master's or PhD is a plus.

In general, you will be successful at Pipe if:

• You want to join a fast-growing startup and make a real impact on customers.
• You want to be part of a team that holds each other to high standards.
• You have a strong technical foundation and are passionate about using your skills to help small businesses succeed.
• You take end-to-end ownership of your work and enjoy collaborating across functions.
• You thrive in an environment that values and rewards excellence.

Compensation and Benefits

We are a fully remote company and we believe in taking care of our employees. As a Pipe employee, you'll receive:

• The best equipment to help you do your job.
• Flexible vacation and work hours. We believe in a healthy work-life balance (really!)
• Excellent health, dental, and vision insurance.
• Generous parental leave for anyone who is growing their family, regardless of gender.
• Great colleagues! We value a culture of authenticity, humility, and excellence. We want you to make a mark on our culture.

The annual US base salary range for this role is $180,000 - $220,000. This salary range may be inclusive of several career levels at Pipe and will be narrowed during the interview process based on a number of factors, including the candidate's experience, qualifications, and location.