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Earnest empowers ambitious professionals to make confident financial decisions and build the life they envision.
Earnies are committed to helping borrowers move forward with confidence by offering smarter borrowing options with a clearer path to taking control of their debt. If you’re as passionate as we are about our mission, read more below, and let’s build something great together.
The Staff Data Scientist will report to Sr. Manager, Data Science + Pricing.
You will be the primary owner of risk data science at Earnest, driving the development of sophisticated credit risk, underwriting, fraud detection, collections, and loss forecasting frameworks that directly shape portfolio health and the unit economics of our lending products. You will lead high-impact modeling initiatives, mentor junior team members, and serve as a key cross-functional partner to senior stakeholders across Risk, Product, Capital Markets, Engineering, and Finance.
As the Staff Data Scientist, you will:
- Own the end-to-end development and delivery of Risk Science systems — including credit risk scoring, default prediction (PD/LGD), fraud detection, and collection scores — that balance loan volume growth with risk-adjusted profitability.
- Lead the design, backtesting, and validation of credit risk models and loss forecasting frameworks, applying advanced statistical techniques and machine learning to evaluate portfolio performance.
- Serve as the internal subject matter expert on credit risk modeling, loss dynamics, and risk-based decisioning engines, shaping how the organization approaches credit policy and risk management.
- Independently lead high-visibility, cross-functional risk modeling projects from conception through production deployment, proactively managing timelines and stakeholder expectations.
- Translate complex risk models, loss projections, and analytical findings into clear, compelling narratives for senior leadership, the Risk Committee, and non-technical stakeholders.
- Mentor and provide technical guidance to junior and senior data scientists, helping establish modeling standards, robust governance, and a culture of analytical rigor.
- Partner closely with Risk, Pricing, Product, Capital Markets, Engineering, and Compliance leadership to align risk strategies with company-wide growth, funding requirements, and regulatory obligations.
- Oversee the deployment of scalable, production-grade risk and fraud pipelines in collaboration with data and ML engineering teams, including automated monitoring, stress testing, and alerting.
- Partner closely with the MRM team for best practices in model validation, fair lending analysis, model governance, and comprehensive technical documentation across the data science team.
About You:
- 8+ years of experience in Python/R and SQL.
- 7+ years of professional experience in quantitative risk modeling, credit risk analytics, fraud analytics, or applied data science
- Master's/PhD in Economics, Statistics, Computer Science, Operations Research, or a related quantitative field
- Prior experience in consumer lending, fintech, or financial services with direct technical ownership of credit underwriting or fraud models.
- Strong expertise in credit risk modeling, default prediction (PD, LGD, EAD), survival analysis, machine learning algorithms (e.g., Gradient Boosted Trees, Logistic Regression), and risk-adjusted return frameworks.
- Deep proficiency in model lifecycle management, model validation, backtesting, and monitoring applied to credit or fraud risk environments.
- Proven track record of independently owning and delivering complex, cross-functional modeling initiatives end-to-end.
- Experience mentoring/managing junior data scientists and contributing to team-wide technical and governance standards.
- Strong communication skills with the ability to influence stakeholders and present technical concepts clearly to senior audiences.
Even Better:
- Solid understanding of financial metrics, consumer credit regulations (e.g., FCRA, ECOA/Fair Lending).
- Experience with cloud production platforms such as AWS, GCP, or Azure.
- Proficiency with modern data engineering and analytics tooling such as Looker, Airflow, and dbt.
- Experience leveraging non-traditional data sources or LLM/NLP tools applied to underwriting, fraud detection, or document processing.
Where:
- This role will be based in the US.
Compensation:
A little about our pay philosophy: We take pride in compensating our employees fairly and equitably. We are showcasing a range of your potential base salary based on the roles location. The successful candidate’s starting pay will also be determined based on job-related qualifications, internal compensation, candidate location and budget. This range may be modified in the future.
Salary Range: $216k - $262k USD. Employees are also eligible for an annual performance-based bonus and equity.
#LI- SM
Earnest believes in enabling our employees to live their best lives. We offer a variety of perks and competitive benefits, including:
- Health, Dental, & Vision benefits plus savings plans
- Mac computers + work-from-home stipend to set up your home office
- Monthly internet and phone reimbursement
- Employee Stock Purchase Plan
- Restricted Stock Units (RSUs)
- 401(k) plan to help you save for retirement plus a company match
- Robust tuition reimbursement program
- $1,000 travel perk on each Earnie-versary to anywhere in the world
- Competitive days of annual PTO
- Competitive parental leave
What Makes an Earnie:
At Earnest, our people bring our cultural principles to life. These principles define how we work, how we win, and what we expect of ourselves and each other:
- Every Second Counts: Speed is our competitive advantage. Our customers need better solutions, and the faster we execute, the greater our chance of success.
- Choose To Do Hard Things: We win by tackling the hard things that others avoid, fueled by grit and resilience.
- Pursue Excellence: Great companies, teams, and individuals never settle and are proud of the work that they do. What’s good enough today won’t be good enough tomorrow. Excellence isn’t a destination; it’s a mindset of continuous improvement.
- Lead Together: Our success comes from how we work together. Leadership is not about titles—it is about action. We take ownership, drive results, and move forward as a team.
- Don’t Take Yourself Too Seriously: We take our work seriously, not ourselves. The stakes are high, but a sense of humor keeps us grounded, creative, and resilient.
At Earnest, we are committed to building an environment where our employees feel included, valued, and heard. Our belief is that a strong commitment to diversity, inclusion, equity, and belonging enables us to move forward with our mission. We are dedicated to adding new perspectives to the team and encourage anyone to apply if your experience is close to what we are looking for.
Earnest provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, sexual orientation, gender identity, veteran status, disability or genetics. Qualified applicants with criminal histories will be considered for the position in a manner consistent with the Fair Chance Ordinance.
Skills Required
- 8+ years of experience with Python/R and SQL
- 7+ years of professional experience in quantitative risk modeling, credit risk analytics, fraud analytics, or applied data science
- Master's or PhD in Economics, Statistics, Computer Science, Operations Research, or a related quantitative field
- Prior experience in consumer lending, fintech, or financial services
- Direct technical ownership of credit underwriting or fraud models
- Expertise in credit risk modeling, default prediction using PD, LGD, and EAD, survival analysis, machine learning algorithms, and risk-adjusted return frameworks
- Proficiency in model lifecycle management, model validation, backtesting, and monitoring for credit or fraud risk environments
- Experience independently owning and delivering complex, cross-functional modeling initiatives end-to-end
- Experience mentoring or managing junior data scientists
- Experience contributing to technical and model governance standards
- Strong communication skills and ability to present technical concepts to senior audiences
- Understanding of financial metrics and consumer credit regulations, including FCRA and ECOA/Fair Lending
- Experience with cloud production platforms such as AWS, GCP, or Azure
- Proficiency with Looker, Airflow, and dbt
- Experience applying non-traditional data sources or LLM/NLP tools to underwriting, fraud detection, or document processing
Earnest Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Earnest and has not been reviewed or approved by Earnest.
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Fair & Transparent Compensation — Compensation is considered competitive for many roles and locations, with bonuses and long‑term incentives contributing to total pay. Public postings show explicit salary ranges that help set expectations across functions.
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Leave & Time Off Breadth — Paid time off is described as generous for both exempt and non‑exempt employees. Additional elements like paid holidays, sick time, volunteer time, and flexible schedules strengthen time‑away access.
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Wellbeing & Lifestyle Benefits — A remote‑first model, home‑office stipends, monthly internet/phone reimbursement, and Mac hardware support day‑to‑day flexibility. A distinctive annual travel credit on work anniversaries adds meaningful lifestyle value.
Earnest Insights
What We Do
We empower past, present, and soon-to-be students to maximize their financial futures through thoughtful guidance and impactful products. We build tools that help people feel in control of their financial future, including: 1. Student Loan Refinancing - break free from high interest rates or monthly payments. 2. Private Student Loans - low rates, people-first service, and flexible payments. 3. Debt Management - manage all your accounts and become debt-free sooner. 4. Scholarships - access to thousands of scholarships to help students pay less.
Why Work With Us
We strive to maintain and strengthen diversity and inclusion through employee-led Employee Resource Groups (ERGs) that empower our Earnies to step into leadership roles and drive our culture. Through ERGs, and with the support of our Executives, we provide a safe space for all Earnies to be their true selves.
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