Analyst, Applied Validations

About the Company Pilots don't train with real passengers. Actors don't rehearse with real audiences. Yet, the most consequential decisions in society are often pushed straight to production.

Simile is changing that. We have built the first AI simulation of society, populated by generative agents based on real humans. Our research pioneered the field of AI-based simulation, proving it is possible to model human behavior with high accuracy. Today, we are developing a Foundation Model to predict human behavior in any situation, at any scale.

We have raised $300M in total funding, with a $2B post-money valuation in our latest round. Our investors include Greenoaks, Index Ventures, Hanabi, A*, Bain Capital Ventures, Factory, CVS Health Ventures, and Definition.

About the Role At Simile, accuracy is the product. Enterprise buyers want proof that Simile matches what their customers actually say and do, and as a Solutions Data Scientist, you will own that proof. You'll design and run customer validations and backtests that compare Simile's simulated results against real-world ground truth, then diagnose gaps, explain what the evidence supports, and identify opportunities to improve performance.

You'll work alongside Solutions Consultants and Deployments, and be deeply embedded with our Modeling, Evals, and Data teams. You'll turn one-off studies into repeatable, scalable workflows that build customer trust in a new category of AI. You'll identify patterns across customer validations and work with Modeling, Evals, Data, and Product to turn those findings into improvements, and test whether those improvements address the gaps.

This is a hands-on individual contributor role for someone who enjoys writing code, investigating data, and learning new methods while working at the pace of customer engagements.

In this role, you will:

• Study Scoping: Scope validation and backtest studies with Solutions Consultants, Deployments, and customers. Pick the right use case, agree on metrics and success criteria up front, and flag studies that won't produce a meaningful comparison. Guide teams on which validation approaches are appropriate given current model and product capabilities.

• Instrument Replication: Re-field or adapt customer survey instruments so they can be replicated in Simile for a true apples-to-apples comparison, and document every change.

• Simulation & Comparison: Run simulations and compare them against human ground truth using metrics such as TVD, Spearman rank correlation, and top-K / bottom-K agreement for MaxDiff.

• Hill-Climbing with Discipline: Iteratively improve results against a target metric while keeping strict holdout discipline, so a backtest is never overfit to a customer's known answers.

• Diagnosing Misses: Break down gaps by segment and population to determine whether the issue lies in the model, the agent bank, or the ground-truth data itself. Identify recurring patterns across validations to distinguish isolated issues from broader gaps in model capabilities, population coverage, or methodology.

• Closing the Loop: Translate validation gaps into reproducible examples and concrete recommendations for Modeling, Evals, Data, and Product. Partner with these teams on follow-up experiments, evaluation coverage, and data collection, and retest whether changes address the gaps.

• Reusable Tooling: Build Python pipelines, API-driven experiments, and LLM skills so backtests run reproducibly across clients instead of in one-off spreadsheets.

• Customer Readouts: Write clear readouts showing where the model is strongly aligned and where it is more directional, and present them to technical and business stakeholders.

Requirements Must Haves

• Statistics & Analytical Judgment: A solid foundation in statistics, including sampling, variability, correlation, and bias. You can question data quality, recognize misleading comparisons, investigate differences across populations and segments, and assess whether conclusions are supported by the evidence.

• Learning & Iteration: Demonstrated ability to learn unfamiliar methods and tools quickly, investigate unexpected results, and improve an approach through structured experimentation. You are motivated to build expertise in customer validations and backtesting.

• Technical & AI-Native Workflow: Ability to use Python and APIs to analyze data and run reproducible experiments, including using Claude Code, Codex, or similar coding agents to accelerate the work. You can review and validate the outputs these tools produce.

• Communication & Commercial Judgment: You can turn analysis into a clear narrative, explain results and limitations to technical and business audiences, and connect findings to customer decisions and priorities.

• Collaboration & Follow-Through: You can translate findings into actionable recommendations, work with Modeling, Evals, Data, and Product teams to address gaps, and document lessons that help others move faster.

Nice to Haves

• Validation & Model Improvement: Experience running backtests, evaluating predictive models, or iteratively improving performance against a target metric.

• Evaluation Methods: Familiarity with holdout datasets, data leakage, and testing whether improvements generalize to unseen examples.

• Survey & Measurement Methods: Familiarity with survey design, panel sampling, distributional comparisons such as TVD, choice-based methods such as MaxDiff, or differences between stated intent and observed behavior.

• Research Background: Experience in market research, consumer insights, survey science, behavioral research, or a research-agency analytics team.

• Survey Platforms: Experience with Qualtrics or similar survey platforms and panel providers.

• Generative Model Evaluation & Automation: Experience evaluating LLM outputs or building reusable pipelines, LLM skills, and automations.

• Customer-Facing Technical Work: Experience in solutions engineering, applied data science, technical consulting, or presenting analytical findings to customers.

Compensation & Benefits At Simile, we provide competitive compensation packages that include base salary, equity, and comprehensive benefits.

• Salary Range: $200,000 – $300,000 USD

• Note: Final offers are based on experience, specialized skills, interview performance, and relevant training.

• Equity: Grants are available for eligible roles, subject to board approval.

• Health & Wellness: Comprehensive medical, dental, and vision coverage.

• Time Off: Flexible time off policies to support work-life balance.

Our Process We prioritize thoughtful conversations and clear examples of past work. Our hiring journey is designed to help both sides align on fit, working style, and expectations.

Reapplication Policy: To ensure a fair and thorough evaluation for all applicants, Simile observes a 90-day waiting period before reconsidering candidates for the same role.

Commitment to Diversity & Inclusion Equal Opportunity: Simile is an equal opportunity workplace. We welcome applicants of all backgrounds and identities, valuing an environment where everyone can contribute authentically.

Accommodations: If you require support or reasonable accommodations during the application process due to a disability, please let us know. We are happy to assist.