We are seeking a highly motivated Contractor Data Scientist to join our Customer Success Data Science team, focused on Smart Expert Growth and Retention. In this role, you'll be the analytical backbone for understanding how our consumer and expert experiences work together to drive the best possible outcomes for customers from identifying opportunity, to measuring impact, to uncovering new ways to create value. You'll partner closely with expert operations, product, and marketing to turn data into decisions, and to prove out where combining human expertise with great customer experience delivers the best results. If you're passionate about using hard data to uncover creative, high-impact solutions at the intersection of customer experience and expert-driven growth, we'd love to hear from you!
- Primary BI Tool (Tableau Shop): the team specifically operates as a Tableau shop. Hands-on experience creating dashboards and reporting in Tableau is heavily preferred.
- Core SQL & Pipeline Building: Big Data technologies are "nice-to-have". The true core requirement is strong SQL proficiency for building data pipelines and datasets from scratch, which will be evaluated through a live SQL coding assessment.
- Building Infrastructure: Ability to quickly understand evolving business concepts and independently build foundational datasets and reporting structures where existing data infrastructure may be limited.
- Domain & Operational Metrics Expertise
- Customer Success & Assisted Operations Metrics: Experience analyzing customer service/assisted models, specifically tracking operational metrics like Average Handle Time (AHT), handle duration optimization, and call efficacy.
- Sales Campaign & Conversion Analytics: Familiarity with tracking and measuring inbound upselling/cross-selling (SEGA) and outbound sales campaign performance (OAR).
- Analytical Mindset & Stakeholder Engagement
- Diagnostic Curiosity: High degree of analytical curiosity to proactively dig into metrics and uncover root causes behind why numbers move up or down, rather than simply reporting what happened.
- Proactive Thought Partnership: Ability to act as a thought partner to non-technical business partners. This involves anticipating stakeholder questions, providing actionable recommendations, and suggesting strategic solutions.
- Stakeholder Experimentation Guidance: Skill in advising and guiding business partners on proper experimentation design, helping them formulate hypotheses and structure test readouts effectively.
Responsibilities
Identify Opportunity & Shape Strategy: Bring strategic thinking to where and how expert-driven engagement can create the most value for customers sizing the addressable opportunity, spotting underserved segments, and framing the questions worth answering before jumping to a solution.
Identify Optimal Timing & Targeting: Analyze customer journey and engagement signals to determine the best moments and segments for experts to engage, and build predictive models to flag which customers are most likely to benefit.
Run Experiments & Causal Reads: Design and analyze A/B tests, and apply causal methods (propensity matching, diff-in-diff, synthetic control) when clean randomization isn't feasible, to isolate the true impact of expert engagement on outcomes.
Generate Ideas & Recommendations: Partner with expert operations and product to turn data patterns into new ideas, targeting rules, or expert engagement approaches providing actionable recommendations even when complete data is unavailable.
Build Self-Serve Reporting: Define KPIs (efficacy, incremental lift, opportunity capture) and build standardized dashboards so stakeholders can track performance without one-off requests.
Education: Qualifications
5+ years of experience in product analytics, marketing analytics, or applied data science ideally with exposure to customer experience measurement, growth analytics, or expert/agent-assisted service models
Advanced proficiency in SQL and "big data" technologies (e.g., Databricks, Spark, Redshift, BigQuery); comfort with a BI tool (Tableau, Qlik, Dash)
Solid grounding in experimentation design (A/B/n) and causal inference methods (propensity score matching, diff-in-diff, synthetic control) including comfort applying these when clean experiments aren't feasible
Strong business acumen and strategic thinking able to translate a fuzzy business question into a testable hypothesis and measurement plan
Strong data storytelling skills able to rapidly build clear visualizations and communicate insights to non-technical stakeholders
Excellent communication skills, with the ability to work independently against a defined scope typical of a contract engagement
Bachelor's degree in Engineering, Data Science, Statistics, Mathematics, Computer Science, Economics, or related quantitative field; Master's preferred
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