Decision Scientist (Test and learn, Reporting)

Decision Scientist 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.

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:

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

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

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.

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

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

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

Thought Partnership & Organization Learning

Advocate for test-and-learn as a discipline; educate stakeholders on experimental design, statistical concepts, and best practices

Build organizational capabilities: document playbooks, and share learning across teams

Collaborate with Fractal partners and subject matter experts to stay current on insurance, customer experience, and analytics best practices

Required Qualifications

Education & Experience

Bachelor's degree in Economics, Finance, Statistics, Mathematics, Operations Research, Data Science, or related quantitative field

4+ years of experience in analytics, data science, or quantitative business roles (financial services, insurance, or consumer tech preferred); OR advanced quantitative degree with 3+ years of relevant experience.

Test & Experimentation

5+ years designing and analyzing A/B tests, multivariate tests, and holdout/control experiments

3+ years with Mastercard APT or comparable incrementality testing platforms

Deep expertise in experimental design, power analysis, hypothesis testing, and statistical significance.

Data & Analytics Foundations

Intermediate to advanced SQL: ability to query databases, build custom datasets, conduct exploratory analysis, and validate data quality

Intermediate to advanced Python: data manipulation, statistical analysis, exploratory data analysis, and automation

Strong data visualization skills: ability to communicate findings clearly using Tableau, Looker, Power BI, or similar tools

Understanding of data quality, validation, and documentation; experience integrating data from multiple sources

Analytics & Statistical Methods

Proven competency in statistical techniques: regression analysis, causal inference, cohort analysis, and other quantitative methods

Experience building analytical models and applying them to business problems

Understanding of how to translate statistical findings into business recommendations

Preferred Qualifications

3+ years of hands-on experience with Adobe Analytics, including funnel analysis, customer journey mapping, and experiment performance tracking

Experience building or applying LTV models to evaluate customer value, retention, and lifetime profitability

Track record running test-and-learn programs for insurance products (auto, home, umbrella, or related lines) or financial services products

Experience working in consulting or advisory roles; ability to partner with cross-functional teams and translate technical findings for non-technical audiences

Familiarity with Financial Services and Insurance, customer acquisition, onboarding, or member experience workflows

Pay

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Fractal, it is not typical for an individual to be hired at or