Principal Analyst - Marketing Measurement & Growth

Gen Digital Inc.•New York, NY

4h•$175,000 - $210,000

About The Position

We're seeking a Principal Analyst to join the MoneyLion Marketing Analytics team and take ownership of high-visibility measurement and decisioning work across our growth analytics function. You'll own ambiguous, first-of-kind measurement problems end to end — from business question through analysis design, execution, and leadership-ready readout — across lifecycle engagement, campaign performance and spend efficiency, marketplace funnel conversion, new media channels, and customer value. This is a hands-on senior IC role with real scope: you'll operate as a measurement leader across functions, partnering directly with the analytics lead, data analysts, data scientists, and senior stakeholders in marketing, product, and finance. You'll raise the team's analytical bar through mentorship and review, with the opportunity to grow in scope as the function expands.

Requirements

7+ years in marketing analytics, growth analytics, decision science, or a related analytics role, with increasing scope and seniority
Proven ownership of complex, ambiguous analytics initiatives end to end — including framing the question, designing the approach, and defending the readout with senior stakeholders
Strong experience with measurement methods: experimentation and A/B testing, holdout and geo-based incrementality testing, and attribution interpretation
Track record of translating business definitions into data definitions, with strong business curiosity and a sharp instinct for the use cases and edge cases that must become analysis logic, tests, and measurement guardrails
Proven ability to own analytical design from business and product requirements — writing design documents, evaluating methodological trade-offs, and explaining complex logic to non-technical stakeholders
Experience setting and upholding standards for analytical quality and metric governance — bringing clear points of view and the judgment to defend or revise them with evidence
Comfort solving novel problems without established playbooks, including the emerging class of AI-era measurement challenges
Expert-level SQL and strong hands-on experience with modern cloud data warehouses, ideally Snowflake
Strong data modeling instincts; experience building or contributing to dbt models and governed reporting datasets strongly preferred
Working knowledge of statistics for experimentation and causal measurement
Experience with BI tools such as Looker and Tableau.
Familiarity with AI-assisted analytics and productivity tools such as Claude Code, Cursor, OpenAI, or similar
Bachelor's degree in a quantitative or technical field required

Nice To Haves

Experience mentoring or leading analysts preferred
Fintech, subscription, marketplace, or consumer growth industry experience a plus
Master's degree preferred
Python for analysis, validation, or automation preferred

Responsibilities

Own high-visibility analytics initiatives end to end — framing ambiguous business questions, designing the analysis, executing hands-on, and delivering decision-ready readouts to senior leadership
Lead measurement design for major marketing investments: holdout and geo-test design, incrementality analysis, and rigorous test readouts for new and emerging media channels
Serve as the measurement partner to marketing, lifecycle, and marketplace stakeholders — translating business definitions into data definitions, and capturing use cases and edge cases as explicit measurement logic and guardrails
Build decision-ready analyses and reporting for core business concepts such as campaign performance, customer journey, revenue attribution, CAC/ROAS, and customer value
Uphold standards for analytical rigor, QA, and metric governance — ensuring leadership-facing numbers are accurate, explainable, and reproducible
Write production-quality SQL and contribute to the team's dbt models and reporting foundations where needed
Raise the team's analytical standards through review, reusable patterns, and mentorship of analysts
Identify recurring data and measurement issues and design durable, scalable fixes rather than one-off patches
Use AI tools to accelerate analysis, QA, and documentation — and help build the workflows that make the team's output faster and more trustworthy