Senior Manager of Advanced Analytics

About Our Client

Our client is a leading technology company providing an all-in-one platform for payroll, employee benefits, HR, and compliance. They are investing in advanced analytics to drive strategic decision-making across Finance, Marketing, and Product Marketing.

Responsibilities

• Lead and develop the Advanced Analytics function, managing and mentoring a team of data scientists.
• Build and execute an advanced analytics roadmap aligned with business priorities.
• Develop financial forecasting, revenue, customer lifetime value (LTV), and churn models.
• Build marketing mix models (MMM) to measure marketing effectiveness and optimize spend.
• Lead pricing, monetization, conjoint analysis, and product packaging research.
• Apply causal inference, experimentation, incrementality measurement, and statistical modeling.
• Use Python and SQL to build, validate, and implement sophisticated analytical models.
• Partner with senior leadership to translate complex findings into actionable business recommendations.
• Collaborate with Analytics Engineering and Data Governance teams to ensure data quality and model reliability.
• Establish best practices for model validation, experimentation, and AI-assisted analytics.

Qualifications

• 8+ years of experience in advanced analytics, data science, or a related quantitative field.
• 3+ years of people management experience leading data science or advanced analytics teams.
• Deep expertise in at least two areas: forecasting/LTV, marketing mix modeling (MMM), pricing/conjoint analysis, or causal inference/experimentation.
• Strong hands-on Python and SQL experience.
• Advanced statistical knowledge, including regression, time-series forecasting, Bayesian methods, difference-in-differences, and synthetic control.
• Experience developing analytical models that influence financial, marketing, pricing, and strategic decisions.
• Strong business acumen and experience presenting analytical findings to VP-level and executive stakeholders.
• Experience working with incomplete data and selecting appropriate statistical methodologies.
• Familiarity with AI-assisted analytical workflows and model validation.
• Bachelor's degree in Statistics, Economics, Mathematics, or a related quantitative discipline, or equivalent experience.