Data Scientist

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Company Overview

The company is a financial technology business focused on helping small businesses access financial tools through the platforms they already use. Its technology simplifies access to funding, spend management, and savings products while handling the complexities of capital markets, underwriting, servicing, compliance, and customer support.

Responsibilities

Growth

• Design experiments and analyses that expand access to capital while maintaining a healthy portfolio.
• Analyze the full merchant lifecycle, including initial offers, repayment, and repeat capital opportunities.
• Identify opportunities to responsibly extend access to additional financial products.
• Model how prospective partner portfolios could perform on the platform to help evaluate and size new opportunities before launch.
• Analyze tradeoffs between growth, risk, customer outcomes, and portfolio performance.

Portfolio Monitoring & Servicing

• Build and deploy machine learning models, including XGBoost-based models, to forecast merchant repayment behavior.
• Perform feature engineering and develop models through production deployment.
• Use predictive models to inform pricing and sizing decisions.
• Measure the effectiveness of servicing initiatives and determine which interventions improve merchant outcomes.
• Analyze recovery and repayment patterns following outreach to struggling merchants.

Data & Infrastructure

• Build data pipelines that support analysis, dashboards, and machine learning models.
• Develop automated systems and workflows that enable the business to operate efficiently at scale.
• Automate processes such as lien filings, KYC checks, and fraud screening using APIs, machine learning, and LLMs.
• Build internal tools, dashboards, Streamlit applications, and Slack-based tools used by teams across the organization.
• Contribute to data and model infrastructure design.
• Identify and implement tools that improve the efficiency of the broader data science organization.

Team & Organizational Contribution

• Work closely with stakeholders across the business to identify and solve important problems.
• Contribute to recruiting and developing data science talent.
• Participate in seminars, project reviews, and knowledge-sharing activities.
• Document work and proactively share findings and lessons with colleagues.
• Mentor other data science team members.

Qualifications

• Bachelor’s degree or higher in Computer Science, Statistics, Mathematics, Data Science, or a related field.
• Strong statistical reasoning and analytical skills.
• Ability to approach data analysis with a critical and intellectually honest perspective.
• Experience designing concise metrics that support decision-making.
• Knowledge of robust causal inference methods.
• Ability to develop valid and predictive machine learning models.
• Strong business judgment and ability to identify and prioritize important problems.
• Ability to select and apply appropriate data science techniques to business challenges.
• Understanding of how analytical and modeling decisions can affect financial performance, customers, and compliance requirements.
• Strong programming skills with an engineering mindset.
• Ability to write clear, reliable, tested, and maintainable code.
• Experience building or sourcing tools that improve team productivity.
• Ability to contribute to data and model infrastructure decisions.
• Strong communication and collaboration skills.
• Ability to build trusted relationships with stakeholders.
• Commitment to efficiency, intellectual honesty, accountability, and empathy.
• Strong documentation and knowledge-sharing practices.
• Ability to prioritize high-impact work and execute projects with urgency.
• Ability to scope projects rigorously and manage them from end to end.
• Experience mentoring other data science professionals is valuable.
• Management experience is a plus.
• Ability to rapidly learn and adopt emerging AI tools and workflows to accelerate analysis.
• Ability to critically evaluate AI-generated outputs for accuracy, reliability, and usefulness.

Disclaimer

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