Senior Data Analyst, Lifecycle
Confirmed on the employer's own hiring board on Nov 4, 2026. First seen by Alion on Nov 3, 2026. Finom scores B on the Alion truth index.
About The Role
About the role
We are looking for a Senior Data Analyst, Lifecycle to own measurement and experimentation for how new customers activate and existing ones stay.The metrics are yours whatever the lever: CRM campaigns, promos and incentives, onboarding changes in the product, and a next best action layer that decides what each customer should see next. You decide what we test, how we test it and what we do with the answer. You also bring your own hypotheses on where the next win is.
What You'll Do
What you'll do
Join activation and retention initiatives before anything is built, whether they come from CRM, growth or product. Define the primary metric, check against historical data whether the effect is detectable at our volumes, and turn solution-shaped requests into problem-shaped ones.
Design and analyse experiments end to end: hypothesis, guardrails, audience split, MDE and power, stopping rule, and a readout that ends in ship, iterate or kill.
Correct for channel and engagement bias (not everyone opens email, and open and click populations are self-selected). When a clean A/B is impossible, use causal inference: diff-in-diff, matching, synthetic control or geo designs.
Bring hypotheses of your own. Launch a few new experiments across onboarding and the customer lifecycle, and turn one-off wins into recurring segmented flows.
Build deliberately simple models: segmentation, propensity, churn and dormancy scoring. They feed audiences, personalisation and incentive targeting. Over time these come together into next best action: for a given customer, which offer, feature nudge or message to send next, through which channel, and whether to send anything at all. You own the logic and the measurement of whether it beats the current rules.
Automate your own recurring work: audience refresh jobs, readout templates, dashboards for Activation, Onboarding and Retention. Ship models the Databricks way (scheduled jobs write tables, tables map into downstream tools such as Braze) and bring engagement data back into the warehouse to close the loop.
What You Bring
Strong SQL and Python, and a Git-based warehouse workflow with code review.
Experiment design, statistics and metric design you can defend.
BI experience, Power BI/Tableau or similar.
Stakeholder management and business sense: you can push back, reframe a request and land a recommendation with a non-technical audience.
Curiosity and ownership. You run your area without waiting to be asked.
What you bring
Nice To Have
Causal inference methods and a clear sense of when each one is valid.
Clustering and segmentation that ends up in use, not in a slide.
Predictive modelling with honest validation. Uplift modelling is a plus.
Braze or a comparable CRM platform: audiences, canvases, native split testing, channel mechanics and their biases.
Nice to have