Sr. Marketing Data Scientist

One of The Sage Group’s clients is looking for a Senior Marketing Data Scientist in the B2B enterprise SaaS/tech domain. The client is a leading vendor in agile software development and IT space.

Duration: 12 months with potential extension and contract to hire depending on business needs

Location: 100% remote in USA, but must be able to work some PT hours

Compensation: $85hr - $102.00hr

About the role and team:

The Marketing Data Science team partners with product, marketing, and sales to turn data into decisions—designing experiments, building metrics and models, and delivering insights that drive adoption and revenue. In this role, you will join a team of data scientists focused on driving adoption for clients AI product. You will collaborate with your teammates to identify opportunities to engage users across a variety of marketing channels and client product surfaces as we work to build awareness and engagement.

1. Scope of Work

Analytics & Decision Support

• Drive analytical work for the Adoption initiative
• Translate product and GTM questions into clear analytical plans.
• Deep-dive into user behavior, funnel conversion, retention, and monetization.
• Own definition, monitoring, and iteration of core metrics for this area: activation, conversion, WAU/MAU, churn

Modeling & Experimentation

• Own the design and analysis of key experiments for Rovo Adoption.
• Run power analyses, define success metrics and guardrails, ensure statistical rigor.
• Provide clear experiment readouts and recommendations.
• Where appropriate, build and validate models such as: Propensity / churn models aligned with GTM Data Science’s roadmap and Cohort / segmentation approaches to support targeted campaigns.

Data Products, Dashboards & Documentation

• Lead the team to create and maintain reusable SQL and Python assets (queries, notebooks) for the team.
• Build or refine dashboards in Tableau for:
• Executive views (high-level KPIs).
• Operational views (e.g., funnel drop-offs, experiment performance, cohort tracking).
• Document metric definitions, data sources, and analytical frameworks so they can be maintained by FTEs after the engagement.

Stakeholder Partnership

• Act as a data partner for Head of Community & Learning.
• Communicate insights in a way that supports decision-making and prioritization
• Clear written summaries (Confluence) and live presentations.
• Proactively identify opportunities for impact (tests to run, segments to target, friction to remove).

2. Expected Deliverables & Milestones

First 30 days Ramp:

• Data sources and schemas relevant to Rovo Adoption.
• Existing experiments, models, and dashboards.
• Current roadmap and open questions.
• Deliver:
• A short written summary of key metric definitions and known data caveats for the area.

By 60–90 days

• Deliver 1–2 major recommendations for improving Rovo Adoption and related measurement and experimentation
• Deep dive on Rovo Adoption funnel with actionable recommendations.
• Analysis of a critical Rovo engagement experiments.
• Deliver 3-6 month roadmaps for Adoption learning and measurement.
• Experiment and analysis ideas.
• Standard guardrail metrics and success criteria.

By 3–6 months

• Deliver at least one high-impact analytical framework or model, e.g.:
• A propensity model for [segment] used by PM/marketing to prioritize work.
• A standardized funnel or lifecycle framework adopted across the team.
• Demonstrate measurable business impact such as:
• Improved conversion, retention, or monetization in target segments.
• Provide handover artifacts:
• Documented SQL, notebooks, metric definitions, and documents.
• Summary of completed work and open opportunities for the internal team.

3. Experience Overview

• 8+ years total in data science / product analytics, Marketing a strong plus
• Relevant domains: B2B SaaS
• Prior roles at B2B SaaS company working with marketing and engineering partners.
• SQL (complex analytical queries on large datasets).
• Experiment design and statistical analysis.
• Experience with: Databricks, Tableau, Python/R for analysis and modeling, and event-based product data
• Work independently with limited oversight.
• Communicate clearly with senior (Director+), non-technical stakeholders.
• Move from analysis to concrete recommendations and follow-through.