Role Description Scaling.com is becoming a research and advising firm — and this role builds the research half of that identity. We are hiring a Ph.D.-level Research Data Scientist to own broader research analysis across our most valuable asset: a unique, constantly evolving dataset of 500+ Scalers actively applying one framework toward a 10x goal. Your mandate is to reverse-engineer the formula. By studying what our Scalers — especially our highest performers — actually do, you will identify the patterns, moves, and correlations that drive extraordinary scaling, and publish those findings on two tracks: - Monthly white papers for our Scaler community - Peer-reviewed articles in academic business journals This is a research lab of one, with room to become a research team you lead. You will work directly with our founders, Dr. Benjamin Hardy and Blake Erickson, who will guide and sharpen your research direction, and alongside our curriculum and data teams day to day. We are looking for someone who self-manages, sets their own research agenda, and treats this dataset as the obsession of their career. - Analyze our Scaler dataset — strategy call recordings, revenue data, and portal data — to identify meaningful patterns, themes, trends, and correlations. - Study our highest-performing Scalers to extract what differentiates them: the moves they make, what they cut, how their teams reconstitute, and how they raise their floor. - Publish a monthly white paper for the Scaler community on what's working right now — core themes, top-performer behaviors, and measurable outcomes from the last 30 days. - Publish in peer-reviewed academic business journals on a quarterly cadence, building a legitimate research line around the science of scaling. - Lead and guide our data capture — tell us what data we should be collecting from Scalers and strategists, and raise the quality of the dataset over time. - Use AI tools fluently (we use Avoma for call intelligence) to accelerate theme detection and analysis across hundreds of engagements. - Translate findings into digestible, compelling writing — and present them live to our Scaler community at events. - Build the foundation of a long-term research engine and intellectual property base for Scaling.com. Qualifications - A Ph.D. candidate, recent Ph.D. graduate, postdoctoral researcher, or early-career professor in business, psychology, organizational behavior, entrepreneurship, or a related quantitative field. - Strong quantitative and qualitative research capability — you can design a study, run the analysis, and defend the methodology. - Experience with (or a clear path to) peer-reviewed academic publishing in business or social science journals. - Exceptional writing across registers — rigorous enough for an academic journal, clear enough for a founder reading a white paper. - Fluent with AI-assisted research and analysis tools, and energized by a messy, living dataset rather than a clean archival one. - Genuinely obsessed with what drives business scaling — this is your research lab, not a job between jobs. - You've read The Science of Scaling, you connect with its ideas, and you can work inside our framework and language. - Self-managing and independent — you set the agenda, bring findings forward, and don't wait to be directed. Benefits - Paid Time Off – Includes vacation days, sick leave, and company holidays - Remote Work Flexibility – Work from the location of your choice (U.S.-based) with the structure, autonomy, and support to thrive

• Technical interface with suppliers, ensuring compliance with technical requirements. • Development of components related to the suspension system for trucks and buses. • Preparation of proposals and designs aimed at cost reduction in product manufacturing. • Collaboration in and oversight of static and dynamic tests for component validation. • Guidance to CAD and CAE teams during the creation of mathematical models. • Failure analysis and CAE reporting. • Participation in technical forums related to product development. • Conduct technical meetings with suppliers, ensuring clarity and accuracy of information. • Monitor schedules, open issues and milestones using project management tools. • Provide weekly status updates and prepare project progress reports. • Release components by preparing technical documentation.

• Design, develop, and deploy causal inference models (e.g., uplift modeling, synthetic control, double machine learning) to understand the true drivers of customer loyalty and measure the incremental impact of marketing interventions. • Build robust machine-learning-based forecasting and predictive models for customer lifetime evaluation (LTV) and customer churn. • Establish foundational data modeling frameworks for a brand-new Business Unit, transforming raw transactional data into scalable features. • Analyze complex customer behavior, purchase patterns, and engagement metrics to build strategies for direct revenue generation. • Perform large-scale data extraction, transformation, and analysis using SQL. • Partner with marketing, product, and business teams to understand loyalty requirements and translate business problems into analytical solutions. • Present model insights and recommendations to senior client stakeholders, clearly communicating the difference between correlation and causation. • Lead workshops and customer analytics strategy discussions with clients. • Implement and operationalize models in cloud environments.

• Ship the models that power personalized in-app experiences — predicting what each member is likely to want next based on real-time behavior, family context, and engagement history. • Detect meaningful patterns in product usage data — communication, scheduling, and activity signals — and build the models that turn those patterns into in-app suggestions, alerts, and guidance for families. • Help replace fixed-placement UI logic with algorithmic decisioning that adapts every time someone opens the app. • Build the churn, retention, and activation models that help Product understand which behaviors predict long-term value and which interventions are worth shipping. • Develop predictive segmentation, propensity, and uplift models, refreshed continuously through automated pipelines so the rest of the business is always acting on current signal. • Partner with Finance on cohort-level subscriber and revenue forecasting, LTV by acquisition source, and sensitivity analysis on the assumptions that matter most. • Build pricing, promo, and refund-risk models that quantify the financial impact of monetization decisions before we ship them. • Train, deploy, and monitor models in Databricks and AWS, using Claude Code as your primary engineering interface. • When agentic delivery makes sense, wrap models in managed agents that surface predictions into the workflows stakeholders already use. • Move the team from "what happened" to "what would happen if" — uplift modeling, geo-tests, and A/B analysis grounded in proper causal framing.