Senior Data Scientist II, Core & Agentic Automation

About the Role:

Role Responsibilities:

• Define core data science priorities across teams, deciding which questions and investments to pursue or decline
• Help deliver our core company KPI framework, including its driver tree and sensitivity modeling
• Lead reusable, company-wide analytical frameworks and automation
• Personally design, build, evaluate, and maintain agentic analytical workflows that use certified metrics to investigate KPI changes, generate evidence-backed hypotheses, and support follow-up analysis, with monitoring, failure handling, and human review where needed.
• Design reusable frameworks with evaluation and evidence traceability
• Set definitions and targets for certified metrics with owners, and resolve conflicting definitions with partners
• Build KPI trees and decompositions that connect product and business drivers to MAU, retention, ARPU, revenue, and margin
• Choose defensible models and quantify the direction, sensitivity, predictive strength, and time horizon of KPI-to-North-Star relationships
• Validate leading indicators out of sample, distinguishing arithmetic identities, predictive associations, and causal effects
• Build scenarios and defensible team targets
• Set measurement standards and defend identification assumptions, guiding decisions when randomization or causal evidence is limited
• Select observational methods when needed, using diagnostics and robustness checks
• Measure accuracy, adoption, reduced bespoke analysis, and time to decision
• Build reproducible, tested analytical workflows in SQL and Python
• Partner with Data Engineering on gold tables and data marts
• Shape leadership strategy and team accountability, aligning Finance and Product on targets, trade-offs, and when evidence warrants a change
• Partner with Product, Finance, Commercial, and Engineering to drive adoption
• Communicate assumptions and uncertainty clearly
• Mentor scientists and establish reusable KPI, measurement, and modeling standards adopted across teams

Minimum Requirements:

• 8+ years of experience in data science, machine learning, or applied analytics, with demonstrated ownership of complex analytical problems in product-driven environments
• Ability to explain which questions and investments you prioritized or declined, and how quantified KPI relationships changed planning or investment decisions
• Proven ability to define KPIs and their hierarchy with multiple stakeholders, structure complex spaces into tackle-able plans, and establish trusted metric definitions adopted across Product, Finance, Commercial, and Engineering
• Track record of aligning Finance and Product on targets, trade-offs, and when evidence warrants a change
• Experience selecting and validating statistical, predictive, and causal models for business planning, including regression, forecasting, sensitivity analysis, and uncertainty estimation
• Skilled at using models for target setting and scenario planning while distinguishing prediction from causal impact
• Demonstrated ability to set measurement standards and defend identification assumptions when randomization or causal evidence is limited, with enough experimentation knowledge to support product data scientists
• Strong skills in SQL, Python, statistics, causal inference, and forecasting
• Track record of shipping reusable dashboards, diagnostic apps, or analytical frameworks with proven accuracy, adoption, and decision impact, and partnering on productionization of robust SQL/Python assets
• History of mentoring scientists and establishing KPI, measurement, or modeling standards that raise quality across teams
• Hands-on experience automating systems with AI, including personally building and shipping agentic automation used in real analytical or business workflows, whether in production or with internal users
• Ability to explain your contribution and how the system retrieves data, uses tools, and completes tasks
• Proven approach to evaluating agentic systems for numerical and analytical accuracy, traceability, failure handling, and adoption, and reusing that approach beyond a single demo or one-off analysis

Preferred Requirements:

• Consumer product analytics experience connecting acquisition, activation, engagement, retention, and monetization
• Experience owning KPI frameworks, causal measurement, or business planning across multiple teams
• Experience in centralized or platform data science
• Experience with governed self-service analytics