1. What is a Data Scientist at EvenUp?

At EvenUp, the Data Scientist role—specifically at the Staff level—is a strategic pivot point for the company. We are not just a standard SaaS platform; we are a mission-driven organization using AI and data to close the justice gap. Your work directly empowers personal injury lawyers to secure higher payouts and faster settlements for victims who might otherwise be underserved by the legal system.

In this position, you move beyond simple reporting or model building. You act as a thought partner to senior leadership, using economics, causal inference, and advanced analytics to shape our product roadmap and monetization strategy. You will tackle complex, ambiguous problems—such as predicting case outcomes, modeling settlement values, and understanding user churn in a vertical SaaS context. You are building the analytical foundation that allows EvenUp to scale from a high-growth startup to an industry standard.

2. Getting Ready for Your Interviews

Preparation for the Staff Data Scientist role requires a shift in mindset from "how do I solve this?" to "why does this matter and how do we measure it?" You should approach your preparation with the goal of demonstrating not just technical fluency, but also economic intuition and strategic leadership.

Causal Inference & Econometrics – EvenUp operates in a domain where A/B testing is not always feasible or ethical. We evaluate your ability to identify causal impact in observational settings using techniques like difference-in-differences, instrumental variables, or propensity score matching. You must show you can derive truth from messy, real-world legal data.

Strategic Product Thinking – We look for candidates who can link statistical findings to business outcomes. You will be evaluated on your ability to define KPIs for activation and retention, and how you use data to influence pricing and packaging decisions.

Technical Communication & Storytelling – As a Staff-level contributor, you will interface with non-technical stakeholders, including lawyers and executives. We assess your ability to distill complex Bayesian models or survival analyses into clear, actionable narratives that drive commercial decisions.

Ambiguity Resolution – Legal data is unstructured and complex. We evaluate how you frame loose business questions ("How do we increase case value?") into scoped, solvable analytical problems.

3. Interview Process Overview

The interview process at EvenUp is rigorous and designed to test both your depth in econometrics and your breadth in product strategy. Generally, the process begins with a recruiter screen to align on the mission and your background, followed by a conversation with a Hiring Manager (often a Head of Data or similar). This stage focuses on your past impact and your interest in the intersection of law and economics.

Following the screens, you will likely encounter a technical assessment. For a Staff role, this often takes the form of a take-home case study or a deep-dive live session where you are asked to solve an open-ended business problem using data. The final stage is an onsite loop (virtual or in-person) comprising multiple panels. These panels cover technical depth (coding/stats), product sense (metrics/strategy), and values alignment. Expect the team to dig deep into why you chose specific methods in your past work, not just how you implemented them.

This timeline illustrates the typical flow from application to offer. Note that for the Staff Data Scientist role, the "Technical Deep Dive" often involves a presentation component where you walk through a strategic analysis or a past project, defending your methodological choices to a panel of peers and leadership.

4. Deep Dive into Evaluation Areas

To succeed, you must demonstrate expertise across three core pillars: Applied Economics/Stats, Product Strategy, and Technical Execution.

Causal Inference and Applied Economics

This is the differentiator for this role. We need to understand the "why" behind user behaviors and case outcomes. Be ready to go over:

• Observational Studies: Techniques for measuring impact when randomization isn't possible (e.g., Synthetic Control, DiD).
• Econometrics: Price elasticity modeling, demand forecasting, and utility theory.
• Experimentation: Designing experiments in a B2B SaaS environment where sample sizes may be smaller than B2C.
• Advanced concepts: Heterogeneous treatment effects and instrumental variable analysis.

Example questions or scenarios:

• "How would you estimate the lift in settlement value provided by our software without running a randomized control trial?"
• "We want to change our pricing model. How do you model the potential impact on churn and ARR using historical data?"

Statistical Modeling & Machine Learning

While strategy is key, you must be hands-on with the math. Be ready to go over:

• Survival Analysis: Critical for understanding "time to settlement" or customer churn timelines.
• Forecasting: Time-series analysis for ARR and usage metrics.
• Bayesian Inference: Updating beliefs as new evidence (case data) comes in.

Example questions or scenarios:

• "Walk me through how you would build a model to predict the probability of a customer churning in the next 3 months. What features are most critical?"
• "How do you handle right-censored data when modeling the duration of a legal case?"

Product & Business Strategy

You must act as a bridge between data and the business. Be ready to go over:

• Metric Frameworks: Defining North Star metrics and counter-metrics for product health.
• Funnel Analysis: Identifying drop-off points in the user journey (from lead to settlement).
• Monetization: Using data to inform packaging tiers or add-on features.

Example questions or scenarios:

• "If engagement drops by 10% but ARR remains flat, how would you investigate the root cause?"
• "How would you determine if a new feature is successful if adoption is low but user satisfaction is high?"

5. Key Responsibilities

As a Staff Data Scientist, your daily work involves high-leverage activities that shape the future of EvenUp. You are responsible for translating complex legal and behavioral datasets into strategic recommendations. This means you aren't just pulling data; you are identifying the questions we should be asking. You will develop forecasts for critical business metrics like ARR, conversion, and churn, providing the executive team with the visibility needed to steer the company.

Collaboration is central to this role. You will partner deeply with Product, Engineering, and Operations to embed data into the product roadmap. This includes leading the analytics strategy for key areas—designing the metric frameworks for new products and establishing best practices for experimentation. Furthermore, you are a builder; you will help evolve our analytical infrastructure, creating scalable self-serve tools and instrumenting events to ensure data quality. Mentorship is also expected; you will raise the bar for analytical rigor across the organization, helping junior analysts grow their skills in data storytelling and causal inference.

6. Role Requirements & Qualifications

We are looking for a blend of academic rigor and startup grit.

• Must-have skills:

  • Experience: 6+ years in data science or applied economics, preferably in high-growth tech or SaaS.
  • Causal Inference: Proven ability to apply statistical techniques (DiD, IV, etc.) in complex, observational settings.
  • Technical Stack: Fluency in SQL and Python or R. Experience with large-scale data systems.
  • Modeling: Strong grasp of time-series, forecasting, survival analysis, and Bayesian inference.
  • Communication: Exceptional ability to explain complex economic reasoning to non-technical stakeholders.

• Nice-to-have skills:

  • Education: Advanced degree (PhD preferred) in Economics, Econometrics, or Statistics.
  • Domain: Prior experience in LegalTech, B2B SaaS, or subscription-based business models.
  • Tools: Familiarity with BI tools like Looker or Tableau and experimentation platforms.
  • Leadership: A track record of mentoring junior talent and elevating scientific standards.

7. Common Interview Questions

The following questions are representative of what you might face. They focus heavily on your ability to apply advanced statistical concepts to real-world business problems. Do not memorize answers; instead, practice structuring your thoughts to show how you navigate ambiguity.

Technical & Methodological

• How do you validate a model when ground truth labels (e.g., final case settlement) take months or years to mature?
• Explain the difference between a fixed-effects and random-effects model. When would you use one over the other in a SaaS context?
• How would you design a metric to measure the "fairness" of a settlement offer generated by our AI?
• Describe a time you used a quasi-experimental method to make a product decision. What were the assumptions, and how did you test them?

Product Sense & Strategy

• We are considering a usage-based pricing model versus a flat subscription. How would you analyze the potential revenue impact?
• A key dashboard shows a sudden spike in user churn. Walk me through your debugging process.
• Define a "successful user" for EvenUp. How does this definition tie back to our long-term revenue goals?
• How do you prioritize between improving model accuracy by 1% versus shipping a new, less accurate feature that users are asking for?

Behavioral & Leadership

• Tell me about a time you had to convince a skeptical executive to change their strategy based on your data analysis.
• Describe a situation where the data was inconclusive. How did you advise the team to proceed?
• How do you handle a situation where a Product Manager wants to ship a feature despite negative experiment results?

8. Frequently Asked Questions

Q: Do I need a background in law or legal studies? No, a legal background is not required. However, intellectual curiosity about the legal domain and a willingness to learn the mechanics of personal injury law (settlements, demands, liability) is essential. We value domain expertise but can teach the specifics of the industry.

Q: How "hands-on" is this Staff role? Very hands-on. While "Staff" implies leadership and strategy, this is an Individual Contributor (IC) role. You are expected to write code, build models, and query data yourself, in addition to guiding strategy and mentoring others.

Q: What is the primary difference between this role and a standard Machine Learning Engineer? This role leans heavily into inference, strategy, and economics. While you will build models, the focus is on understanding causality and driving business decisions (pricing, retention, product direction) rather than just optimizing prediction latency or deploying models to production.

Q: What is the work culture regarding remote vs. in-office? This is a hybrid role. We believe in the value of in-person collaboration for complex problem solving. The expectation is to work at least 3 days a week from one of our hubs in San Francisco or Toronto.

9. Other General Tips

Focus on "The Narrative": At EvenUp, data is useless if it doesn't tell a story. When answering case study questions, don't just dump numbers. Structure your answer: "Here is the context, here is the hypothesis, here is the method, and here is the business recommendation."

Brush up on SaaS Metrics: Even if your background is in pure economics, ensure you are fluent in SaaS terminology (ARR, NRR, CAC, LTV, Churn). You will need to apply your statistical knowledge to these specific business levers.

Highlight Ambiguity: The legal space is not black and white. Show that you are comfortable making reasonable assumptions when data is missing or messy. Explicitly state your assumptions during the interview.

Demonstrate Passion for the Mission: We are here to help victims get justice. Candidates who show genuine passion for this social impact—beyond just the technical challenge—stand out.

10. Summary & Next Steps

The Staff Data Scientist role at EvenUp is a rare opportunity to apply high-level econometrics and causal inference to a massive, untouched market. You will be instrumental in defining how we monetize our products and how we prove value to our customers. This role demands a unique combination of technical depth in statistics and broad strategic business acumen.

To succeed, focus your preparation on causal inference techniques, SaaS metrics, and communication skills. Be ready to defend your methodologies and show how your work drives revenue and user value. If you are ready to use data to level the playing field for injury victims, we want to hear from you.

The compensation for this role is significant, reflecting the high level of expertise and ownership required. The range provided includes base salary; equity and other benefits are additional components of the total rewards package, making the total opportunity highly competitive for the San Francisco market.