Responsibilities

• Apply statistical, machine learning, and econometric methods to large datasets to evaluate channel performance and isolate the causal impact of marketing and sales activity on a multi-touch B2B sales cycle
• Build attribution models and investment frameworks that inform Spellbook's channel spend, helping marketing and finance understand which messages resonate with which segments at each stage of the funnel
• Partner with Marketing Ops, Sales, and Engineering to unify data across first- and third-party sources, so every growth decision is made with full context
• Drive experimental design across acquisition, activation, and retention surfaces, enabling fast, low-cost iteration on new channels and messaging
• Shape the data culture at Spellbook by improving the tools, processes, and systems the broader team uses to make decisions at scale

Requirements

• Bachelor's degree or above in Math, Economics, Statistics, Computer Science, Engineering, or another quantitative field, with 5+ years of industry experience as a Data Scientist
• Strong Python skills (numpy, pandas, scikit-learn, etc.) across exploratory analysis, predictive modeling, and applied ML for marketing and growth problems
• Strong SQL skills (Snowflake, BigQuery, or Redshift preferred)
• A track record of shipping measurable improvements alongside growth and product teams, with proven leadership
• A strong point of view on the marketing experimentation lifecycle — hypothesis generation, experimental design, statistical analysis, and A/B testing best practices
• Deep familiarity with marketing attribution, martech, and the modern privacy landscape
• Comfort operating in a fast-paced startup environment where problems are solved through iterative technical solutions

Preferred

• Experience at a high-growth startup, ideally in B2B SaaS
• Familiarity with enterprise or legal-tech sales cycle metrics
• Experience with the modern data stack (Fivetran, Snowflake, dbt, Looker, Hex, or equivalents)
• Familiarity with data orchestration tools (Airflow, Dagster, Prefect)
• A strong perspective on data science engineering practices — data modeling, version control, testing, and documentation

Benefits

• Embrace autonomy and accountability in a flexible work environment; we focus on outcomes and empower you to determine how to get the job done
• Access our company-paid group benefits for you and your family, with $1,000 towards mental health support
• Disconnect during our holiday closure and take advantage of our generous time off policies throughout the year
• Enjoy monthly paid meals, an annual wellness allowance to support your well-being and parental leave top-ups as your family grows
• Secure your stake in our success; you’ll receive competitive stock option grants as a pivotal early employee