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. Common Interview Questions

3. 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.

4. 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.

5. 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?"