Experimentation Analytics Lead

RESPONSIBILITIES:
Kforce has a client in San Francisco, CA that is looking for a Lead Experimentation Analyst to drive rigorous experimentation and measurement across our digital product portfolio. This role will serve as a practitioner and subject matter expert for website experimentation, partnering with Product, UX, Analytics, and Engineering teams to design high-quality experiments, evaluate results, and ensure product decisions are grounded in statistically sound evidence.

Responsibilities:
* Partner with Product and UX teams to develop hypotheses, success metrics, and experimentation strategies for digital experiences
* Design A/B and multivariate experiments, including audience allocation, sample sizing, test duration, primary KPIs, and guardrail metrics
* Conduct statistical power analysis and determine minimum detectable effects for proposed experiments
* Analyze experiment results and determine statistical and practical significance
* Apply advanced experimentation techniques including variance reduction, sequential testing, heterogeneous treatment effect analysis, and other appropriate statistical methodologies
* Evaluate experiment validity, including sample ratio mismatch, instrumentation issues, novelty effects, traffic imbalance, and other sources of bias
* Develop measurement approaches for tests with uneven audience allocation or operational constraints
* Conduct segmentation and follow-up analyses to understand how experiment effects differ across devices, customer groups, product categories, and experiences
* Establish experimentation standards, governance, and best practices across digital product teams
* Partner with analytics implementation teams to validate tracking and ensure experiment data quality

REQUIREMENTS:
* 5+ years of experience in experimentation, product analytics, statistics, data science, or a related analytics field
* Strong statistical foundation including hypothesis testing, power analysis, confidence intervals, variance, and experimental design
* Demonstrated experience designing and analyzing digital A/B tests and multivariate experiments
* Strong SQL skills and experience working with large-scale behavioral or event-level datasets
* Experience with experimentation platforms such as Adobe Target, Optimizely, Statsig, LaunchDarkly, or similar tools
* Familiarity with advanced experimentation approaches such as CUPED, sequential testing, Bayesian methods, causal inference, or heterogeneous treatment effects
* Strong understanding of ecommerce product metrics and digital customer journeys
* Ability to identify experiment quality issues and explain statistical concepts clearly to non-technical stakeholders
* Experience with Python or R preferred
* Excellent communication skills and ability to turn experiment results into clear business recommendations

The pay range is the lowest to highest compensation we reasonably in good faith believe we would pay at posting for this role. We may ultimately pay more or less than this range. Employee pay is based on factors like relevant education, qualifications, certifications, experience, skills, seniority, location, performance, union contract and business needs. This range may be modified in the future.

We offer comprehensive benefits including medical/dental/vision insurance, HSA, FSA, 401(k), and life, disability & ADD insurance to eligible employees. Salaried personnel receive paid time off. Hourly employees are not eligible for paid time off unless required by law. Hourly employees on a Service Contract Act project are eligible for paid sick leave.

Note: Pay is not considered compensation until it is earned, vested and determinable. The amount and availability of any compensation remains in Kforce's sole discretion unless and until paid and may be modified in its discretion consistent with the law.

This job is not eligible for bonuses, incentives or commissions.

Kforce is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, gender identity, national origin, age, protected veteran status, or disability status.

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