• Own and evolve Comfrt’s measurement framework alongside partners across MMM & MTA (Northbeam), incrementality (Workmagic), and experimentation • Lead Marketing Mix Modeling (MMM) initiatives in partnership with measurement partners and internal stakeholders • Design and evaluate geo tests, holdouts, lift studies, and quasi-experimental frameworks • Quantify true incremental impact across marketing channels, campaigns, creators, and growth initiatives • Identify saturation points, diminishing returns, and marginal efficiency curves across paid and organic channels • Continuously improve model accuracy, interpretability, and decision-making reliability • Develop models and analytical frameworks that improve CAC efficiency, LTV forecasting, contribution margin visibility, and retention economics • Analyze customer cohorts to identify drivers of high-value and durable customer behavior • Partner with Growth and Finance teams on forecasting, budget allocation, and investment planning • Help distinguish short-term performance gains from long-term brand and customer value creation • Build frameworks that measure the relationship between acquisition strategy, retention quality, and enterprise value creation • Build scalable analytical systems and production-grade models using Python and SQL • Partner with Data Engineering & Data Analytics & Insights to improve data quality, metric governance, and experimentation infrastructure • Standardize and maintain trusted business metrics including CAC, LTV, MER, contribution margin, retention, and incremental ROI • Collaborate with Engineering and BI teams to operationalize models into dashboards, workflows, and reporting systems • Ensure measurement systems remain scalable, reliable, and actionable as the business grows • Translate complex analyses and modeling outputs into clear, actionable business recommendations • Influence executive decision-making around growth investments, profitability, and long-term business quality • Partner cross-functionally across Growth, Finance, Product, Data Engineering and Technology teams. • Help establish a culture of rigorous, data-informed decision-making across Comfrt

• Lead the design, implementation, and deployment of ML/DL algorithms. • Apply quantitative analysis and an insightful presentation of data to see beyond the numbers and understand how our users interact with our growth product. • Apply data mining techniques, perform statistical analysis, and build high-quality predictive models. • Meet the business vision and strategy in accordance with the product innovation backlog. • Collaborate with the product and engineering teams to deliver usable and informative analytical interfaces.

• Forecasting model ownership • Build and iterate production grade demand and volume forecasts • Evaluate models with backtesting and real-world performance • Maintain stability with retraining, drift monitoring, and failure handling • Improve models based on operational outcomes • Define input data contracts for optimization models including capacity, constraints, and demand • Transform raw data into model-ready features and constraints • Validate solver outputs and identify infeasibility, constraint conflicts, and scaling issues • Trace issues back to data assumptions and constraint design • Convert outputs into usable schedules and recommendations • Compare model output to operational reality and explain gaps • Run scenario analyses under different constraints • Improve pipelines that support forecasting and optimization quality • Test assumptions against real-world variation • Explain models and outputs to non-technical users including operators and clients • Translate tradeoffs between accuracy, feasibility, and constraints • Deliver clear recommendations tied to business outcomes • Participate in client conversations and defend model behavior

Role Description As a Safety Statistician – Risk & Safety Analysis, you will play a critical role in how Torc evaluates, communicates, and makes decisions about the safety of its autonomous driving systems. You will influence the design and execution of statistically rigorous analyses that inform safety assurance strategies, engineering priorities, and risk-based decision making. Your work will directly influence how safety performance and risk are measured, understood, and acted upon across the organization. This is a technical role focused on applied statistics and decision support, not dashboarding, experimentation platforms, or generic ML product analytics. - Employ statistically sound analyses to answer high-impact safety and regulatory questions, including how system performance translates to risk. - Apply statistical methods to quantify and assess risk using a variety of data sources including large-scale time-series data (e.g., vehicle and sensor data) and structured safety datasets. - Bridge safety and engineering teams by translating complex analyses into information engineers can act on. - Develop automated, production-ready analysis workflows that support continuous safety monitoring. - Select and defend appropriate statistical approaches for sparse, noisy, or rare-event data, applying Bayesian and frequentist methods and leveraging machine learning techniques where appropriate. - Communicate statistically defensible findings to technical leaders, safety stakeholders, and executives. Qualifications - Advanced degree in Statistics or a closely related field. - M.S. with 3+ years of experience, or PhD with 1+ years of experience. - Strong background in applied statistics, safety analysis, and risk estimation. - Experience in autonomous vehicles, adjacent safety-critical domains (automotive, aerospace, defense, robotics, rail, etc.), or comparable actuarial experience. - Experience working with complex, real-world datasets rather than clean or purely academic data. - Hands-on experience using Python for analysis (SQL and/or R a plus, but not required). - Ability to communicate statistical concepts clearly to non-statistical audiences. - Comfort operating independently as a technical leader in a cross-functional, distributed environment. - Domain knowledge in Bayesian methods. Requirements - Experience applying Bayesian methods to estimate risk using disparate data sources (such as simulations and naturalistic driving). - Background applying statistics to engineering or physics-based systems. - Familiarity with time-series analysis, uncertainty quantification, or rare-event modeling. - Experience supporting executive or external stakeholder decision-making requiring quick turnarounds, balancing analytical rigor with timeliness. Benefits - A competitive compensation package that includes a bonus component and stock options. - 100% paid medical, dental, and vision premiums for full-time employees. - 401K plan with a 6% employer match. - Flexibility in schedule and generous paid vacation (available immediately after start date). - Company-wide holiday office closures. - AD+D and Life Insurance.