Senior AI Data Analyst - Sales

Join Pigment: The AI Platform Redefining Business Planning Pigment is the AI-powered business planning and performance management platform built for agility and scale. We connect people, data, and processes in one intuitive, feature-rich solution, empowering every teamfrom Finance to HRto build, adapt, and align strategic plans in real time. Founded in 2019, Pigment is one of the fastest-growing SaaS companies globally. Industry leaders like Unilever, Snowflake, Siemens, and DPD use Pigment daily to make more informed decisions and confidently navigate any scenario. With a team of 600+ across Paris, London, New York, Toronto, San Francisco and Austin, we've raised nearly $400M from top-tier investors and were named a Visionary in the 2024 Gartner Magic Quadrant for Financial Planning Software. At Pigment, we take smart risks, celebrate bold ideas, and challenge the status quoall while working as one team. If you're driven by innovation and ready to make an impact at scale, wed love to hear from you. Senior AI Data Analyst GTM AI & Analytics Team Owns the data strategy powering the global Sales org pipeline health, forecasting, sales planning (capacity, quota, territory, headcount), and pipeline/revenue growth across enterprise accounts. Sits between analytics, data engineering, and business strategy; partners with Sales leadership, RevOps, Marketing, and Finance. AI-Native Component Contributes to GTM Cortex, the company's GTM analytics/orchestration layer: Builds analytical "skills" as version-controlled code (atomic to composite), tested and maintained in GitHub Works in Claude Code, querying live Pigment data through the Pigment MCP References a central KPI Catalog rather than redefining metrics ad hoc Converts recurring sales questions into reusable skills instead of one-off analyses Drives adoption of Analyst/Modeler AI agents across Sales Key Responsibilities Executive & Operational Analytics Owns dashboards: pipeline generation, coverage, forecast, win rates, sales cycle, quota attainment, ARR, net-new vs. expansion Builds Pigment models and opportunity-level datasets for pipeline, capacity, revenue Standardizes metrics/definitions across teams and regions Planning & Capacity Modeling Analytics backbone for annual and in-year sales planning cycles Builds/maintains capacity, quota, productivity models in Pigment Models headcount and territory scenarios; runs what-if analysis against historical performance Data Architecture & Automation Designs data pipelines from Salesforce, Pigment, Gong, Netsuite, etc. Partners with Data Engineering on dbt/warehouse transformations Builds AI skills on Pigment MCP for recurring reports and event-driven alerts Owns data quality, governance, documentation Business Partnership Analytics partner to Sales leadership and regional orgs Answers questions like: what's driving win rate/cycle changes, where is coverage weakest, how does pipeline quality Enablement Builds role-based reporting for reps, managers, execs Trains Sales teams on data use in pipeline reviews, QBRs, forecast calls Drives adoption of AI-driven workflows Required Qualifications 58+ years in Data Analytics, Analytics Engineering, or BI Advanced SQL; experience with Snowflake/BigQuery/Redshift Strong SaaS/sales metrics background (ARR, pipeline gen, coverage, win rate, quota attainment, forecast accuracy) Hands-on BI/planning tools (Pigment, Looker, Tableau, Power BI, Mode) Experience supporting enterprise Sales/RevOps, including GTM planning Genuine interest in AI-native analytics (LLM-assisted analysis, skills-as-code, governed data access) Nice to Have Pigment experience Salesforce, Gong, Clari, Outreach/Salesloft, Segment, or Amplitude Claude Code or MCP-based data access experience dbt, Python, or analytics engineering background B2B enterprise SaaS experience Familiarity with complex, multi-segment sales/forecasting motions Success Metrics Sales leadership relies on insights for pipeline, forecast, capacity planning Risks surfaced early, not just reported Dashboards/AI skills used in every pipeline review and QBR Growing library of reusable analytics skills reduces one-off requests Planning cycles run on these models Data is trusted, consistent, embedded in workflows We are targeting a total comp packag .