Senior Data Scientist (Customer Success)

Location: Mountain View or San Diego, California, United States

Base pay: $162,500–$220,000 annually in Mountain View; compensation may also include a cash bonus, equity rewards, and benefits.

Company

hirify.global is a global financial technology platform offering products including TurboTax, Credit Karma, QuickBooks, and Mailchimp.

What you will do

• Define KPIs, success metrics, causal levers, and measurement standards for onboarding, time-to-value, product adoption, customer health, retention, and growth.
• Provide strategic recommendations from diverse and incomplete data, including clear ship-or-iterate decisions.
• Design and own randomized experiments, quasi-experiments, predictive models, and causal inference approaches for customer success and service investments.
• Build predictive models for customer health scoring, churn and expansion propensity, next-best action, and durable customer segmentation.
• Create executive-level visualizations and storytelling, and build automated, monitored, reproducible self-serve data products with Data Engineering.
• Collaborate with AI, product, digital, customer support, sales, and marketing teams to integrate AI solutions and influence decisions.

Requirements

• 4+ years of experience in product analytics, web analytics, customer care analytics, or other customer experience analytics; fintech experience is preferred.
• Advanced SQL, big data technologies such as Redshift, Spark, Hive, or BigQuery, and BI tools such as Tableau, Qlik, or Plotly Dash.
• Deep expertise in experimentation design, including A/B/n and bandits, and causal inference methods including Propensity Score, DiD, and Synthetic Control, including for small-sample or non-randomized populations.
• Experience framing ambiguous business problems into decisions, building and owning predictive models through their lifecycle, and delivering measurable business outcomes.
• Experience building and maintaining automated, monitored, and reproducible data products.
• Strong business acumen, hypothesis development, data storytelling, visualization, communication, and cross-functional collaboration skills.
• Flexibility to shift priorities in a fast-paced environment.
• Bachelor's degree in Engineering, Data Science, Statistics, Mathematics, Computer Science, Economics, or a related quantitative field; a Master's degree is preferred.

Nice to have

• Strong programming skills in Python or R and experience building ML and GenAI models, including automation and custom implementations.
• Experience evaluating AI-native or non-deterministic systems and defining quality when outputs vary.
• Familiarity with accounting, bookkeeping, professional services, or tax.
• Experience solving growth problems at financial technology companies serving consumers or SMBs.
• Experience directing AI tools to build and review data products.

Culture & Benefits

• Competitive compensation with a pay-for-performance rewards approach.
• Potential cash bonus, equity rewards, and benefits under applicable plans and programs.
• Regular compensation comparisons across ethnicity and gender categories to support fair pay.