Data Scientist

About the role

This role is for a Data Scientist who will own the design and execution of data-driven projects that directly support Parafin's mission to grow small businesses. You will partner with stakeholders across product, engineering, and operations to translate ambiguous business questions into rigorous analytical strategies. You will own the full lifecycle of data projects, from initial hypothesis and data collection through model development, validation, and deployment in production environments. The position requires a high degree of intellectual curiosity and skepticism, pushing beyond surface-level metrics to uncover root causes and true business impact. You will be responsible for ensuring that every analysis is methodologically sound, reproducible, and clearly communicated to both technical and non-technical audiences. This role offers the opportunity to work on a diverse portfolio of problems spanning growth, risk, and infrastructure, shaping the future of financial services for small businesses. You will continuously evaluate the performance of your models and experiments, iterating based on empirical evidence to drive measurable outcomes for the company.

Key facts

What you'll do

Design and execute experiments to measure the impact of new financial products and underwriting policies on merchant acquisition, repayment behavior, and portfolio health.

Develop and deploy machine learning models, such as XGBoost and other algorithms, to forecast merchant repayment risk and optimize pricing strategies across our partner ecosystem.

Build and maintain data pipelines using modern data stack tools to ensure the reliability, scalability, and freshness of datasets used for analysis and modeling.

Partner with growth teams to model how a prospective partner's portfolio would perform on our platform, enabling us to size new opportunities and manage risk exposure effectively.

Create dashboards and monitoring tools that provide real-time visibility into key business metrics, allowing stakeholders to track the health of our portfolio and the success of our interventions.

Measure the effectiveness of our servicing efforts by quantifying balance recovery after outreach and identifying interventions that materially improve merchant outcomes.

Automate manual processes such as lien filings, KYC checks, and fraud screens using APIs, machine learning, and large language models to improve operational efficiency.

Develop internal tools, including Streamlit apps, dashboards, and Slack bots, to empower other teams to access data and insights without relying on engineering resources.

Conduct rigorous statistical analysis using robust causal inference methods to ensure that observed business impacts are genuine and not driven by confounding factors.

Design metrics and frameworks that guide strategic decision-making, balancing growth objectives with risk management and portfolio sustainability.

Study the full merchant lifecycle - from first offer and repayment to repeat capital - to identify opportunities to responsibly extend more credit and improve retention.

Collaborate with cross-functional partners to translate business problems into analytical approaches and ensure alignment on success metrics.

Champion data quality and governance by implementing best practices for data collection, storage, and documentation across the team.

Participate in regular seminars and project reviews to share insights, receive feedback, and stay aligned with the evolving priorities of the business.

Requirements

Bachelor's Degree (or higher) in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.

Demonstrated ability to apply statistical reasoning to complex business problems, designing concise metrics that drive actionable decisions.

Experience using robust causal inference methods to evaluate program impact and test hypotheses in observational settings.

Strong proficiency in developing and validating machine learning models, including experience with techniques that produce accurate and predictive results.

Proven track record of business acumen, including identifying priority problems, gathering requirements, and translating analysis into concrete business recommendations.

Comfortable working with large, multi-dimensional datasets and writing clean, efficient SQL queries to extract insights.

Experience building and deploying models into production environments, ensuring reliability, scalability, and maintainability of data products.

Strong written and verbal communication skills, with the ability to explain complex analytical concepts to non-technical stakeholders.

Nice to have

Experience with streaming data architectures and real-time analytics platforms.

Practical notes

This is a full-time position based in San Francisco, California.

Candidates must be authorized to work in the United States without sponsorship for this role.

The listed compensation range is not available for this position.

Application review will begin immediately and continue until the role is filled.

Candidates are encouraged to highlight relevant projects and technical contributions that demonstrate their skills and impact.

This role requires a high level of ownership, intellectual rigor, and collaboration within a fast-paced, mission-driven environment.