Role Description The Data Science team at Block turns insights from our unique datasets into actions that improve the customer experience every day. In this role, we're looking for a Data Scientist to own the modeling and experimentation at the core of how Square prices globally. You'll build the elasticity and willingness-to-pay models, design and run the pricing experiments, and stand up the analytical infrastructure that makes pricing measurable and controllable — shaping pricing strategy and deal-desk automation through the models and experiments you build. You Will - Model price elasticity and willingness-to-pay across segments, geographies, and payment methods, and quantify the trade-off between margin, conversion, and merchant retention. - Design, run, and read out pricing experiments (A/B, difference-in-differences, and bandit-based dynamic tests) and translate results into recommendations that shape strategy. - Decompose merchant economics across interchange, scheme, and risk-cost layers to identify where pricing can flex and where it can't. - Build the pricing intelligence that powers Square's agentic deal tooling (DealBot) — rate recommendations, ROI and pre-approval logic, guardrail configurations, and mispricing detection — so quotes are fast, accurate, and within guardrails at scale. - Evaluate and monitor the AI systems you ship — pre-deployment testing for accuracy, boundary and edge cases, and bias in rate recommendations, and in-production monitoring for accuracy, drift, and mispricing — so agentic pricing tools stay reliable as the business changes. - Own end-to-end execution across the stack — analysis, pipeline, ETL, experimentation, and visualization. - Approach problems from first principles, using a variety of statistical and modeling techniques to understand customer behavior and price response. - Build and maintain the pricing analytics the team relies on — price realization, margin leakage, discount-waterfall, and win/loss analyses — as self-serve dashboards and curated datasets. - Measure the impact of AI-driven pricing automation with causal methods (interrupted time series, difference-in-differences) on deal velocity, quote acceptance, and margin. - Write code to process, cleanse, and combine data sources into curated ETL datasets easily used by the broader team. - Partner closely with cross-functional stakeholders across Finance, Risk, Product, and go-to-market teams, translating complex technical and AI concepts clearly for non-technical audiences. Qualifications - A bachelor degree in statistics, data science, economics, or similar STEM field with 7+ years of experience in a relevant role OR a graduate degree in statistics, data science, economics, or similar STEM field with 5+ years of experience in a relevant role. - Fluency in causal inference and experimentation, with hands-on experience modeling price elasticity or willingness-to-pay. - Prior exposure to a pricing-adjacent domain a strong plus — risk-based pricing (payments, lending, insurance), pricing science, or deal pricing analytics. - Advanced proficiency with SQL and data visualization tools (e.g. Tableau, Looker, etc). - Experience with scripting and data analysis programming languages, such as Python or R, including using them to evaluate AI system behavior. - Gone deep with cohort and funnel analyses, with a solid understanding of statistical concepts such as selection bias, probability distributions, and conditional probabilities. - Comfort leveraging AI tools to accelerate modeling and analysis, and a working understanding of generative AI architectures — LLMs, RAG systems, and agentic AI; experience building, testing, or evaluating LLM-powered systems in production a strong plus. Requirements - Technologies We Use and Teach: SQL, Snowflake, etc. - Python (Pandas, Numpy). Benefits - Remote work. - Medical insurance. - Flexible time off. - Retirement savings plans. - Modern family planning.

Staff Data Scientist Location: Palo Alto, California Employment Type: Full-time (W-2) Work Arrangement: Hybrid (2 days per week in the Palo Alto office) Salary: $210,000–$240,000 USD annually About the Role We are looking for a highly strategic Senior or Staff Data Scientist to design, build, and own the end-to-end data framework that defines our business health: Unit Economics. In this role, you won't just build standalone models; you will connect the dots between customer acquisition, multi-product lifecycles, complex healthcare reimbursement cycles, and operational cost structures. Your work will serve as the financial and analytical source of truth, directly influencing how we allocate marketing spend, price our products, manage retention, and project long-term profitability. You will sit at the intersection of Data Science, Finance, Marketing, and Operations, acting as a critical strategic partner to executive leadership. What You’ll Do Unified LTV & Reimbursement Modeling - Bridge Estimated vs. Realized LTV: Develop sophisticated lifetime value models that account for the volatility of healthcare reimbursements and the time value of money. - Predictive Reimbursement Rates: Build models to predict actual reimbursement rates across a complex mix of insurance allowables and self-pay tracks, closing the gap between theoretical revenue and cash-in-hand. - Integrate Margin Constraints: Establish the foundational frameworks that incorporate operational realities—such as state-by-state clinician licensing costs and wage ranges—ensuring our LTV calculations reflect true contribution margins. Cross-Product Attribution & Portfolio Optimization - Blended Contribution Margin: Optimize "basket composition" and cross-sell dynamics between our physical supplement lines and clinical services to maximize total margin. - Multi-Touch & Cross-Product Attribution: Build advanced attribution models (Markov chain, ML-based) to quantify the interplay between product lines—specifically tracking how supplement purchases drive clinical visit adoption and vice versa. - Price Elasticity: Design and analyze pricing experiments for supplement products to identify optimal margin-maximizing price points without degrading long-term subscriber retention. Causal Inference & Growth Intelligence - Influence the CAC Decision Curve: Utilize your LTV and margin frameworks to influence the marginal LTV curves that marketing uses, helping them determine the exact point of diminishing returns on ad spend. - Causal Churn Intervention: Move beyond simple churn prediction. Build uplift models to identify which at-risk customers will respond positively to specific interventions (e.g., targeted offers, clinical outreach), preserving margin by avoiding unnecessary discounting on "sure things" or "lost causes." Strategic Macro-Simulation - Systemic Stress-Testing: Build stochastic (Monte Carlo) macro-simulations to help leadership and finance stress-test our business model. You will answer questions like: "If a major insurance payer shifts an allowable rate in a key state, how does that impact our payback period and portfolio margin?" What You Bring Technical Skills - Advanced Modeling & Stats: Mastery of predictive modeling and Causal Inference techniques (e.g., uplift modeling, propensity score matching, synthetic controls, or diff-in-diff). - Production-Grade Engineering: Proven experience architecture - building, deploying, and maintaining production-grade machine learning models. You write clean, modular, and well-tested code that integrates seamlessly into downstream workflows. - Expert-Level Evaluation: Deep expertise in model evaluation methodologies, backtesting, and validation. Because your models directly impact financial forecasts and pricing decisions, you have a rigorous approach to error analysis, cross-validation, and drift detection. - Attribution & LTV: Proven track record building attribution models (algorithmic or heuristic) and handling survival analysis for churn and retention forecasting. - Programming & Querying: Advanced proficiency in Python for complex statistical analysis, alongside expert-level SQL for manipulating large data streams. - Simulation Design: Experience structuring systemic business simulations or stochastic modeling. - Modern AI Workflow: Active adoption and mastery of Large Language Models (LLMs) and generative AI tools within your personal development workflow to accelerate coding, debugging, documentation, and prototyping. Analytical Capabilities - Unit Economics Intuition: You have a deep, near-obsessive understanding of the relationship between CAC, LTV, payback periods, gross margins, and contribution margins. - Business Acumen: The ability to translate complex statistical outputs into clean, actionable frameworks for the CFO, CMO, and executive leaders. You know how to influence cross-functional roadmaps with data. - Strategic Problem Structuring: Ability to take vague, complex business questions and break them down into answerable, high-impact analytical components. Experience - 8+ years of experience delivering high-impact data science solutions. - Master’s or PhD in Economics, Econometrics, Applied Statistics, or a related quantitative discipline. - Ideally, your background includes time in Marketplaces, Healthcare operations, or D2C subscription businesses. - Demonstrated progression in scope and impact, with a history of acting as a strategic partner to finance and operations teams. Interview Process: Recruiter Screen- 30 mins Hiring Manager Screen- 45 mins Technical Screen- 1hr Panel Interviews- 2-3 hours + Lunch in the office in Palo Alto At this time, Midi is unable to provide visa sponsorship. Candidates must be authorized to work in the U.S. without current or future sponsorship needs. The Salary range is 210-240K and will depend on experience. Midi pays a competitive base salary, plus equity and benefits. While you’re waiting for us to review your portfolio, here’s some fun content to check out https://www.youtube.com/watch?v=1px7i6MVjNg #LI-PS1 At this time, Midi is unable to provide visa sponsorship. All Candidates must be authorized to work in the United States without current or future sponsorship needs. Please note that all official communication from Midi Health will come from an @joinmidi.com email address. We will never ask for payment of any kind during the application or hiring process. If you receive any suspicious communication claiming to be from Midi Health, please report it immediately by emailing us at careers@joinmidi.com. Midi Health is an Equal Opportunity Employer. We are committed to pay equity and ensure that all qualified applicants receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status. Our compensation philosophy is based on fair, objective criteria and the impact of the role, regardless of an applicant’s salary history.

• Own and scale the Data Domain Model, ensuring domain ownership is clear and newly established domains are seamlessly onboarded. • Champion the development and execution of enterprise data policies (security, retention, access). • Monitor cross-domain compliance, coordinate remediation with domain owners, and maintain evidence packages for SOC 2, GDPR/CCPA, and internal audits. • Own the day-to-day operations of Toast’s Business Glossary. • Drive widespread adoption of our data catalog and governance tools across business and technical teams through office hours, training programs, and clear documentation. • Lead regular data governance maturity assessments across business units, converting findings into actionable improvement roadmaps.

• Develop, validate, and maintain SDTM and ADaM datasets using R following CDISC standards. • Support TLF (Tables, Listings, Figures) generation in R or SAS as needed. • Write efficient, reproducible, and well-structured R scripts for clinical data analysis and reporting. • Collaborate with statisticians, data managers, and clinical teams to understand programming requirements. • Perform QC checks, reconcile data issues, and ensure deliverables meet regulatory expectations (e.g., FDA, EMA). • Contribute to programming workflows, documentation, and version control best practices. • Support automation initiatives and R-based pipeline development. • Utilize SAS for legacy studies or where SAS support is required.