Data Scientist

DESCRIPTION

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 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. Master’s Degree desirable. Minimum 3-5 years of relevant industry experience.

Key points:

• The candidate should have minimum of 8 years of experience. Hands on.
• Being based in the Bay Area is a plus but not mandatory.
• Regular office visits (about once a week) significantly increase the likelihood of a long-term engagement.
• Experience as a Data Scientist in Production Systems (rather than Product Engineering) is beneficial.
• The ideal candidate is a business-facing consultant with in-depth experience in:
• Identifying testing opportunities.
• Creating hypotheses.
• Analyzing user behavior, data, and extracts.
• Generating business insights.
• Influencing business decisions based on data-driven inferences

Roles & Responsibilities :

ESSENTIAL QUALIFICATIONS

• Expertise in experimentation and statistical hypothesis testing (e.g., determining sample sizes/power analysis, applying statistical tests, confidence intervals).
• Strong communication and presentation abilities, with the skill to convey analyses to a wide range of stakeholders, including data scientists, creatives, and marketing leaders.
• Extensive experience in digital analytics, with the capability to extract insights from diverse quantitative and qualitative data sources.
• Proficiency in data querying (SQL) and familiarity with scripting languages such as Python or R.
• Excellent time management skills, with the ability to oversee multiple A/B testing projects in various stages of development.
• A team-oriented mindset, with a preference for collaborative work.

Highly advantageous:

• Experience in advanced statistics, econometrics, or causal inference.
• Familiarity with predictive analytics and machine learning algorithms, such as regression, decision trees, clustering, and neural networks.