Lead Data Scientist, Growth Marketing

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Role Summary:

We are seeking a visionary Lead Data Scientist, Growth Marketing to define the analytical strategy and drive the quantitative engine for Slack’s global web presence.

In this senior-most individual contributor (IC) role, you will bridge deep technical data science with high-level business strategy. You will move beyond optimizing local web conversion metrics to connect web behavioral insights directly to overarching business impact—such as customer acquisition, enterprise pipeline generation, and lifetime value.

As a technical authority and strategic thought partner to Marketing, Product, and Engineering leadership, you will lead our global web experimentation framework, architect advanced behavioral models, and elevate the analytical capabilities of the broader organization. You will serve as a lead IC who actively mentors, coaches, and guides data analysts and data scientists, with the potential or opportunity to functionally lead and manage quantitative teams.

Key Responsibilities:

Strategic Business Impact & Growth Vision

• Macro Connection: Connect top- and mid-funnel web behavior directly to core business outcomes, enterprise ARR, pipeline velocity, and customer lifetime value (LTV).

• Strategic Roadmap: Partner with executive leadership (VPs, SVPs) across Marketing, Product, and Growth Engineering to align web analytics and experimentation strategies with multi-year company growth targets.

• Opportunity Prioritization: Identify macro trends, unaddressed user segments, and high-leverage growth opportunities through deep quantitative research and exploratory data analysis.

Advanced Experimentation & Causal Inference Leadership

• Global Framework Ownership: Lead and evolve Slack’s web experimentation strategy, setting institutional standards for statistical rigor, hypothesis formulation, power analysis, and sample size estimation.

• Methodological Depth: Deploy advanced statistical techniques, such as variance reduction (CUPED), sequential testing, synthetic controls, and quasi-experimental designs to measure true incremental lift in complex digital environments.

• Experimentation Culture: Drive best practices across product and growth teams to eliminate bias, manage interference/network effects, and scale experiment volume without compromising data integrity.

Technical Architecture & Behavioral Data Science

• Analytics & Privacy Stack: Serve as the principal architect for the digital measurement ecosystem (e.g., GA4, Adobe Analytics, server-side tracking, GTM) in a privacy-first, first-party data landscape.

• Predictive & Behavioral Modeling: Build and operationalize session-stitching, multi-touch pathing analysis, propensity modeling, and advanced behavioral clustering (e.g., K-Means) to profile visitors into targetable segments.

• Scalable Data Ecosystem: Collaborate with Data Engineering to design robust web data pipelines and automated reporting layers that support self-service insights for growth teams.

Team Leadership, Mentorship & Functional Management

• Technical Mentorship: Act as a force multiplier by coaching, mentoring, and guiding data analysts and junior/mid-level data scientists on statistical methodology, SQL/Python code quality, and strategic thinking.

• Team & Pod Leadership: Demonstrate the aptitude and leadership (formal or informal) to lead project pods, manage sprint priorities, or transition into team management as the analytics organization expands.

• Rigor & Governance: Establish peer review standards for analytical models, code repositories, and experimental documentation across the growth analytics team.

Executive Advisory & Communication

• Leadership Storytelling: Synthesize complex statistical findings into clear, impactful executive narratives for VPs, SVPs, and C-suite leaders, framing analytics around strategic ROI and business trade-offs.

• Cross-Functional Alignment: Translate quantitative insights into actionable product and marketing roadmaps that align cross-functional stakeholders around high-impact growth initiatives.

Candidate Requirements & Competencies:

Experience & Technical Mastery

• 8+ years of experience in Data Science, Quantitative Analytics, or Growth Analytics, ideally within high-growth B2B SaaS, enterprise tech, or large-scale digital platforms.

• Expert Causal Inference & Statistics: Deep expertise in p-values, confidence intervals, Bayesian vs. Frequentist approaches, CUPED, quasi-experiments, and causal inference frameworks.

• Advanced Coding & Data Science Tools: Fluent in SQL and Python or R for pathing analysis, propensity modeling, and machine learning/clustering techniques.

• Web Stack & Privacy: Proven experience navigating server-side tracking, tag management (GTM/GA4), and first-party data strategies in privacy-restricted environments.

• Visualization & Reporting Architecture: Experience building automated, executive-ready dashboarding systems (e.g., Tableau, Looker).

Leadership & Strategic Mindset

• Leadership & Mentorship Track Record: Demonstrated experience guiding, mentoring, or managing data analysts and data scientists. Prior team management experience or clear potential/desire to manage quantitative teams.

• Big-Picture Orientation: Ability to connect granular digital experimentation metrics to enterprise-level P&L, CAC/LTV dynamics, and broader business strategy.

• Executive Presence: Outstanding oral and written communication skills with a proven track record of influencing executive decision-making.

Unleash Your Potential

When you join Salesforce, you’ll be limitless in all areas of your life. Our benefits and resources support you to find balance and be your best, and our AI agents accelerate your impact so you can do your best. Together, we’ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future — but to redefine what’s possible — for yourself, for AI, and the world.

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