Marketing Data Scientist
Why this role matters:
We are looking for a Data Scientist to own our demand and revenue intelligence capabilities. You will sit at the intersection of marketing strategy and commercial outcomes, building the predictive models and analytical frameworks that tell us where growth is coming from before it arrives. Your work will directly shape how we reach the right audiences, invest in the right channels, and accelerate pipeline - turning marketing activity into a measurable, forward-looking revenue engine.
Your key responsibilities:
Analyze and model the relationship between marketing investment, channel performance, and revenue outcomes, connecting spend to growth
Design and maintain reporting views and self-serve analytics assets for internal stakeholders and marketing partners; making model outputs and performance data accessible, consistent, and actionable across teams
Identify high-value market segments and audiences through data modeling to inform how and where we compete
Develop personalization models and frameworks that enable tailored customer experiences across channels, from dynamic content and offers to next-best-action recommendations
Collaborate with Marketing and Sales teams to define forecasting methodologies and embed models into operational workflows
Translate complex model outputs into clear, actionable narratives that drive strategic decisions at the executive level
Contribute to the development of AI-ready data structures in Snowflake, including clean feature tables, scoring outputs, and audience segments that serve as reliable inputs for marketing automation and AI-driven decisioning
What you'll bring:
Strong understanding of the full B2B revenue funnel - from awareness and demand generation through pipeline to closed revenue - and how data flows across it
Strong business acumen with the ability to connect predictive model outputs to strategic marketing and revenue decisions
The ability to influence RevOps, Sales, and Marketing stakeholders through data-led storytelling
A rigorous yet pragmatic approach to forecasting - knowing when to build a sophisticated model and when a well-structured regression is enough
A proven self-starter comfortable operating in fast-moving, commercially-driven environments
A working fluency with AI tools and LLM-based workflows — able to leverage AI to accelerate analysis, prototype models, and operate effectively in an AI-first data environment
Required:
8 years of experience in Data Science, Revenue Analytics, or a related field
Salesforce data experience: proven hands-on experience with Salesforce data across the full object model (Leads, Contacts, Accounts, Opportunities, Campaigns), and a deep understanding of how marketing and sales data interconnects within Salesforce
Snowflake data warehouse experience: demonstrated experience querying, modeling, and working with data in Snowflake, including SQL-based data modeling and working with structured analytical data sets
Experience modeling pipeline generation, conversion rates, and revenue velocity across the full funnel
Tools & Technologies:
Salesforce - full object model (Leads, Contacts, Accounts, Opportunities, Campaigns), SOQL, Salesforce administration, Salesforce Einstein Analytics / Tableau CRM
Python - scikit-learn, statsmodels, Prophet, pandas, NumPy for forecasting and modeling
SQL - advanced querying across CRM and marketing data sources
Data Visualization - Tableau, Power BI, Salesforce-native dashboarding
Marketing Automation Platforms - Marketo, HubSpot, or Pardot (data structure familiarity)
Cloud Data Platforms - Snowflake, AWS, GCP, or Azure for data extraction and model deployment
Version Control - Git/GitHub or GitLab
Spreadsheet & Presentation Tools - Excel, Google Sheets for stakeholder-facing outputs
What we consider a plus:
Experience with customer lifetime value (LTV) modeling and churn prediction
Familiarity with marketing automation platforms and how they feed into Salesforce (e.g., Marketo, HubSpot)
Experience with lead-to-revenue funnel analytics in a B2B SaaS environment
Working knowledge of digital marketing platforms and their underlying data structures
Advanced degree (Master's or PhD) in a quantitative field such as Statistics, Economics, or Computer Science
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Disclaimer: The posted range represents the good faith salary for this role at the time of posting. Final compensation is determined by factors including (but not limited to) geographic location, relevant experience, specific skill sets, and internal equity.