Data / ML / Analytics

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