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Tier-1 brand, remote role, metro location, and broad ML skillset increase candidate competition.
Applied ML and product-usage modeling skills transfer across industries, though B2B SaaS domain knowledge moderately matters.
Role demands specialized ML, CDP activation, and experimentation skills despite no explicit years requirement.
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Design, build, and maintain predictive models (propensity-to-upgrade, engagement scoring) to identify customers likely to expand AI product usage.
Collaborate with Lifecycle Marketing and Marketing Operations to activate models within the Customer Data Platform and convert outputs into actionable customer segments.
Develop datasets from behavioral and product usage data, validate and continuously improve model performance, and support experimentation to optimize customer expansion.
Experience Requirement: Several years in data science, analytics, or applied machine learning within B2B SaaS or subscription-based environment.
Proficiency in Python or equivalent data science tools with experience in feature engineering and model evaluation.
Experience working with large complex datasets such as behavioral logs, product usage, or campaign data.
Experience with model deployment or activation within analytics platforms or CDPs (e.g., Treasure Data) is preferred but not explicitly required.
Experienced in building predictive models that influence customer growth and expansion decisions in a B2B SaaS or subscription context.
Able to translate complex model outputs into business insights and collaborate cross-functionally with marketing, product, and commercial teams.
Skilled in experimentation design, model validation, and communicating technical results to non-technical stakeholders for actionable impact.