AI Customer Lifetime Value Prediction Engineer

Farion Data

Remote · $130k – $160k / year · Full-time · Posted 5 months ago

This role is no longer accepting applications

Don't miss the next role like this

Create a free profile with your skills. We match you to new AI roles as they open, and companies hiring can contact you directly.

Create free profile, 2 minutes

Similar open roles you can apply to now:

See all open AI jobs

This role is no longer accepting applications

Don't miss the next role like this

Create a free profile with your skills. We match you to new AI roles as they open, and companies hiring can contact you directly.

Create free profile, 2 minutes

Similar open roles you can apply to now:

See all open AI jobs

About the role

Marketing budgets are wasted when you spend $200 acquiring a customer who will churn in 3 months. Our CLV prediction models tell marketing teams exactly how much a customer is worth over their lifetime — not as a segment average, but as an individual prediction updated weekly with behavioral signals. The modeling challenge is combining purchase history (transactional data), engagement signals (email opens, app sessions, support tickets), and external factors (seasonality, competitive actions, economic indicators) into a single probabilistic forecast. We use survival models for churn timing, regression models for spend prediction, and hierarchical Bayesian models for segment-level priors — all orchestrated into a weekly scoring pipeline that serves predictions to 25 marketing teams. You'll own the model architecture and evaluation framework. The analytics team has two mid-level analysts who handle data prep and client reporting; you'll handle the modeling decisions that determine prediction quality.

Fully remote.

Responsibilities

Design and maintain CLV prediction models combining transaction, engagement, and external signals
Build survival models for churn timing and spend regression for revenue forecasting
Implement hierarchical priors for stable predictions across customer segments
Run weekly scoring pipelines serving predictions to 25 marketing teams
Evaluate model accuracy against realized customer outcomes
Guide two analysts on data preparation and feature engineering

Requirements

5+ years in predictive analytics with CLV, churn, or revenue modeling
Experience with survival analysis, Bayesian methods, and probabilistic forecasting
Strong Python, SQL, and distributed computing skills
Understanding of marketing metrics and customer behavior patterns
Ability to translate model outputs into marketing-actionable recommendations