About Prism Data

Prism Data is building the future of credit risk assessment using modern data science and transaction-level financial data. Our API-based platform enables banks, fintechs, and lenders to use automated cash flow underwriting—analyzing detailed banking history in real time—to improve credit decisions, expand approvals, and reduce losses. We believe cash flow tells a more accurate story than traditional credit scores alone, and we’re building products that help credit providers make faster, fairer decisions. Prism transforms raw transaction data from any source into highly structured, explainable features and predictive risk scores, including the market-leading CashScore®. Our products sit at the intersection of data engineering, machine learning, and regulated financial decisioning—and are trusted by some of the largest financial institutions and fastest-growing fintechs in the country.

About the role

At the heart of Prism’s offering is Data Science, not just as a technical function, but as the core product. The Data Scientist helps connect Prism’s analytical capabilities to customer needs while contributing to the continued improvement of those products. Prism is seeking an early-career Data Scientist to join its growing team. This role is designed for a strong quantitative practitioner who can apply data science fundamentals to real business problems, produce accurate and reproducible work, communicate clearly with technical and non-technical audiences, build knowledge of Prism’s cash flow underwriting domain, and develop through regular guidance and feedback. It presents a rare opportunity to make a measurable difference at a critical inflection point for both Prism and the credit scoring and lending industries.

This is a hybrid, in-person role in New York City or San Diego with 3 days in-office per week.

Responsibilities

• Support customer proof-of-concept and business analyses
  • Complete defined workstreams within proof-of-concept analyses and backtests of Prism’s scores and attributes on prospect and client portfolios, including sample preparation and validation, outcome-definition support, coverage and scoreability analysis, performance measurement, benchmark comparison, and segment analysis.
  • Validate analysis inputs and outputs, reconcile key counts and metrics, document assumptions, and raise data-quality or methodological questions early.
  • Create accurate, review-ready deliverables, visualizations, and presentations that explain statistical performance, key findings, limitations, and business implications for customer use cases.
  • Participate in customer and prospect discussions or presentations alongside senior team members; explain assigned analyses, respond to questions within scope, and document or follow up on items that need additional review.
  • Build working knowledge of customer use cases, lending verticals, portfolio characteristics, and how Prism’s scores and attributes create value.
• Research and build domain knowledge
  • Research assigned statistical and machine learning techniques, portfolio benchmarks, or customer use cases; implement focused prototypes; and summarize their applicability, benefits, and limitations.
  • Build a high-level understanding of the cash flow and credit underwriting landscape, including core terminology, key players, product differentiators, and regulated lending considerations.
  • Learn Prism’s products, analytical methods, data flows, documentation standards, and model governance practices, and apply that knowledge correctly to assigned work.
• Contribute to analytical products
  • Complete clearly scoped modeling and analytical tasks that support Prism’s cash flow underwriting product suite, including transaction categorization, income detection and verification, recurring inflow and outflow logic, attribute generation, and credit risk scores.
  • Prepare, clean, explore, and validate transaction-level data, applying established approaches to missingness, duplicates, coverage variation, timing irregularities, and source-to-source inconsistencies.
  • Engineer features; train and compare baseline and selected models; and evaluate performance using appropriate metrics, with guidance from more senior data scientists.
  • Produce reproducible code, analysis outputs, and concise documentation, and incorporate review feedback before work is used in product or customer contexts.
• Support product quality and delivery
  • Assist with product implementation, validation, quality assurance, and monitoring using established processes and tools.
  • Investigate routine questions about data quality and model behavior, document findings, and escalate non-routine issues or unexpected results.
  • Maintain analysis assets and contribute to repeatable workflows that support consistent, timely delivery.
  • Identify areas that could be improved and raise them to a manager or senior team member with clear supporting observations.

How You’ll Succeed

Success in this role is defined by four ways of working. We will look for their foundations during hiring and support you in strengthening them through coaching and experience.

• Learning & Self-Leadership: You’ll approach challenges with curiosity and a can-do attitude, seek help early when needed, and apply feedback with a growth mindset and low ego.
• Technical Judgment: You’ll apply data science fundamentals soundly, learn new methods quickly, use established approaches to resolve routine issues, and escalate non-routine questions at the right time.
• Collaboration & Communication: You’ll build productive working relationships, listen carefully, and explain quantitative work clearly to audiences with different levels of technical fluency.
• Reliable Execution: You’ll stay organized and detail-oriented, work through obstacles resourcefully, meet commitments or flag risks early, and learn from mistakes without repeating them.

Qualifications

• Education: Bachelor’s degree in Statistics, Data Science, Math, Industrial Engineering, or a similar quantitative discipline required. Master’s or PhD is a plus.
• Professional Experience:
  • Typically 0-2 years of professional experience in data science, statistics, quantitative analytics, or a related field.
  • Relevant internships, research, capstone work, or substantial applied projects may count toward experience.
  • Demonstrated experience applying statistical or machine learning methods to real datasets and drawing supported conclusions from the results.
  • Demonstrated ability to communicate quantitative concepts, analytical results, limitations, and business implications clearly in writing and verbally to business audiences with quantitative and non-quantitative backgrounds, with guidance and review appropriate to an early-career role.
  • Exposure to credit risk, financial services, cash flow underwriting, or transaction-level financial data is a plus, but is not required.
• Technical Skills and Knowledge:
  • Working proficiency in Python and SQL.
  • Strong foundation in data preparation, exploratory analysis, feature engineering, and core statistical and machine learning methods, including regression, classification, decision trees, and model evaluation.
  • Ability to understand and explain data flows, analytical methods, model outputs, and limitations.
  • Familiarity with gradient boosting, natural language processing, streamlining code-intensive tasks with LLMs, transaction categorization, or model explainability are all pluses.

Pay

Target base salary for this role is between $95,000 and $115,000 per year. Prism also offers additional equity-based compensation.

Benefits

Prism provides a comprehensive benefit plan, including medical, dental, vision, and 401(k).