We are seeking a Data Scientist to strengthen our ability to identify which customer behaviors and treatments truly drive business outcomes and to turn those findings into better decisions across the customer lifecycle. This role will sit at the intersection of analytics, experimentation, and business strategy, with a primary focus on causal inference, treatment evaluation, and the translation of data into actionable recommendations. The need is grounded in the current CSM mission to generate behavioral intelligence, causal insights, and decision policies, as well as an active roadmap that already includes causal analysis, treatment evaluation, lead indicators, semantic-model development, and related data/measurement priorities across Norton and Freemium.
Why this role is needed
This is not a generic analytics backfill. The role is needed to address a specific capability gap in causal measurement and experimentation support that cannot be fully absorbed by existing analytics capacity or by tooling alone. Current work is already distributed across analytics and reporting, treatments, portal, and data/AI ownership, while emerging agent-based tooling improves access and speed but still depends on strong statistical judgment, sound methodology, and clear experiment design to produce trusted outcomes.
Core responsibilities
Design and execute causal analyses to determine whether changes in behavior, treatments, campaigns, and product experiences have a measurable impact on key KPI’s
Partner with analytics, treatments, product, and data stakeholders to shape experiment design, measurement plans, and success criteria before launches
Support A/B test analysis, quasi-experimental analysis, and post-readouts with clear business interpretation and recommendations
Identify key drivers of KPI movement and translate analytical findings into specific actions for lifecycle strategy, targeting, and treatment design
Help define robust measurement frameworks for lead indicators, customer journeys, and treatment performance
Improve confidence in analysis by ensuring appropriate validation logic, data definitions, and measurement consistency are in place for causal work
Contribute to the evolution of data products, semantic models, and analytical workflows that make experimentation and causal readouts more scalable
Communicate findings clearly to business leaders and cross-functional partners, focusing on implications, tradeoffs, and next-step decisions
Required capabilities
Strong grounding in statistics, experimental design, and causal inference
Experience analyzing A/B tests or other treatment/intervention programs in a business setting
Ability to work with complex behavioral, customer, or lifecycle data and turn ambiguous questions into clear analytical plans
Strong SQL skills and practical experience using Python or similar tools for analysis
Ability to evaluate data quality, identify risks to valid inference, and apply appropriate checks before drawing conclusions
Comfort working across technical and non-technical teams and translating technical findings into business language
Good judgment on when to use descriptive analytics, predictive methods, or causal approaches depending on the decision context
Preferred experience
Experience in subscription, lifecycle, CRM, growth, retention, or customer strategy analytics
Experience with BigQuery, modern cloud analytics environments, or large-scale customer datasets
Familiarity with contribution analysis, driver analysis, uplift thinking, or heterogeneous treatment effects
Exposure to building or supporting reusable analytics assets such as measurement frameworks, standardized readouts, or internal data science tooling
Success measures
Success in this role would include:
Improved quality and speed of causal readouts for key treatments and business initiatives
Better clarity on which interventions materially move retention, expansion, engagement, and related KPI’s
Stronger experimental design and measurement discipline upstream of launches
More trusted and repeatable analytical outputs that business stakeholders can use to make decisions with confidence
Clearer translation of analytical findings into actionable recommendations for roadmap and treatment prioritization
Role profile
Level: Data Scientist
Orientation: Individual contributor
Primary emphasis: Causal inference and experimentation
Key interfaces: Analytics and Reporting, Treatments, Product/Portal partners, Data and AI, and business stakeholders across the customer lifecycle