Sr. Data Scientist with A/B Test
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Title: Sr. Data Scientist with A/B Test
Location: Sunnyvale, CA (Hybrid)
Long Term Contract
KEY QUALIFICATIONS (Please refer to turbo hire for the complete JD)
Expertise in experimentation and statistical hypothesis testing (e.g., sample size determination/power analysis, statistical tests, confidence intervals, etc.).
Strong communication and presentation skills, including presenting analyses to a diverse group of stakeholders, including data scientists, creatives, and marketing leaders.
Outstanding digital analytics experience with the ability to derive insights from multiple quantitative and qualitative data sources.
Strong data querying skills (SQL) and experience with a scripting language (Python or R).
Excellent time management skills and ability to manage multiple A/B testing projects in various stages of development.
Team player who enjoys collaborating with others. Highly beneficial:
Experience with advanced statistics, econometrics, or causal inference.
Experience with predictive analytics and ML algorithms such as regression, decision trees, clustering, and neural nets.
Core responsibilities cover three pillars:
DIGITAL ANALYTICS:
Lead a select number of strategic analyses to provide teams and leaders with actionable
insights regarding opportunities to improve traffic, engagement, and conversion.
Turn optimization hypotheses into A/B test proposals. Partner with Marcom Producers,
Creatives, and Developers to determine the A/B test population, user experience, KPIs, and
runtime requirements.
EXPERIMENT ANALYSIS:
Design experiments, analyze results, develop insights, and summarize recommendations.
Work closely with the A/B Test Platform Engineering Team to ensure the analytics
implementation suits our needs.
RESEARCH & DEVELOPMENT:
Partner with Data Scientists across Marcom, Retail, and Operations on projects to increase the sophistication of our experimentation program and deliver deeper insights about marketing performance.
EDUCATION & EXPERIENCE:
Bachelor s degree with quantitative emphasis. Statistics, Data Science, Mathematics,
Operations Research, Computer Science, Marketing Analytics, or related field.