Decision Scientist (Test and learn, Reporting)

Job description

It's fun to work in a company where people truly BELIEVE in what they are doing! We're committed to bringing passion and customer focus to the business. Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets; an ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work® Institute and recognized as a ‘Cool Vendor’ and a ‘Vendor to Watch’ by Gartner. Please visit Fractal | Intelligence for Imagination for more information about Fractal. Job Title: Decision Scientist Role Overview Fractal Analytics is seeking an experienced Decision Scientist to lead test-and-learn initiatives across insurance products and member experiences. This role combines deep statistical rigor with consulting acumen: you will partner with product, marketing, and operations teams to frame business questions, design experiments, measure incremental impact, and translate results into clear, actionable recommendations. Beyond testing execution, you will leverage data modeling, analytics, and quantitative thinking to solve problems across the full value chain—from member acquisition and onboarding through engagement, retention, and lifetime value. You will understand client requirements, scope problems broadly, integrate disparate data sources, and drive fact-based decision-making across the organization. This is an analytics consulting role. The ideal candidate combines strong experimental design and statistical skills with business acumen, intellectual curiosity, and the ability to translate complex technical findings into clear recommendations for cross-functional stakeholders. Location: San Antonio/ Dallas (Texas) Key Responsibilities PRIMARY FOCUS: TEST-AND-LEARN WITHIN AN ANALYTICS CONSULTING CONTEXT Core Competencies: Experimental Design & Incrementality Testing 5+ years designing and executing A/B tests, multivariate tests, and holdout/control experiments 3+ years with Mastercard APT or equivalent incrementality testing platforms Advanced Statistical Methods Expert-level knowledge of hypothesis testing, power analysis, significance testing, causal inference, regression discontinuity, and other statistical techniques to validate business hypotheses Data & Analytics Foundation Strong SQL and Python; hands-on experience with data integration, quality assurance, and exploratory analysis Ability to work with complex, multi-source datasets Adobe Analytics & Digital Funnel Analysis 3+ years with Adobe Analytics for funnel analysis, customer journey mapping, and experiment performance tracking across web and app experiences Lifetime Value (LTV) & Business Impact Modeling Experience building and applying LTV models to estimate long-term impact of test-and-learn initiatives on customer retention, profitability, and business value KEY RESPONSIBILITIES 1. Problem Scoping & Consulting Partnership Work closely with stakeholders across product, marketing, operations, and strategy to understand business objectives and challenges Translate business questions into testable hypotheses; frame problems in statistical and analytical terms Scope test-and-learn initiatives within the broader value chain—understanding how acquisition, engagement, retention, and LTV connect to overall business objectives Identify data requirements and integration needs; partner with data engineering and analytics teams to prepare datasets 2. Test Design & Experimental Methodology Design A/B tests, multivariate tests, and holdout/control experiments; define treatment, control, and sample allocation strategies Apply power analysis and sample size calculations to ensure statistical rigor; document assumptions and methodology Use Mastercard APT to configure, execute, and monitor test cells and measure incremental lift against control groups Design for real-world constraints: seasonality, holdout group limitations, technical feasibility, and compliance requirements. 3. Data Analysis & Insights Extraction Execute SQL and Python queries to extract, validate, and prepare test results and outcome data Apply statistical testing, confidence interval estimation, and causal inference methods to measure incremental lift and validate results Use Adobe Analytics to track experiment performance, segment results by customer cohort, and identify interaction effects Build LTV models to estimate long-term impact of treatments on customer value, retention curves, and profitability Conduct sensitivity analysis and validate findings against alternative model specifications 4. Recommendations & Actionability Translate incremental lift, LTV impact, and statistical findings into clear, business-focused recommendations Quantify business impact: "Implementing this change will lift acquisition by X%, generating $Y in incremental revenue" Design communication materials and visualizations for peer review, leadership, and operational execution teams Prioritize recommendations by impact, feasibility, and alignment with strategic objectives 5. End-to-End Analytics & Data Integration Identify, gather, and integrate disparate data sources: digital (Adobe Analytics, clickstream), transactional, campaign, and third-party data Apply data quality techniques, validation checks, and documentation standards to ensure analytical rigor Build analytical datasets and dashboards to support ongoing monitoring and learning from test initiatives Document data lineage, assumptions, and methodology to support peer review and compliance requirements 6. Thought Partnership & Organization Learning Advocate for test-and-learn as a discipline; educate stakeholders on ex