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:

• Build and deploy demand forecasting and pipeline prediction models that project revenue outcomes at the campaign, segment, and market level
• Develop and maintain lead scoring, audience propensity, and opportunity scoring models to sharpen targeting and prioritize sales and marketing effort
• Analyze and model the relationship between marketing investment, channel performance, and revenue outcomes, connecting spend to growth
• 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
• Own and shape the revenue data ecosystem, ensuring data integrity, influencing how data is captured, and making it model-ready across systems
• 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

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
• Intuition for how marketing programs, channels, and audiences translate into commercial outcomes
• 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

Required:

• 8 years of experience in Data Science, Revenue Analytics, or a related field
• Proven experience building predictive and demand forecasting models tied to revenue outcomes
• Strong understanding of CRM data ecosystems and how marketing and sales data interconnects – Salesforce experience strongly preferred
• Strong proficiency in Python and SQL
• Experience modeling pipeline generation, conversion rates, and revenue velocity across the full funnel
• Proficiency with data visualization tools (Tableau, Power BI, or similar)

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

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.

Originally posted on Himalayas