Data scientist/ML Engineer: Personalization and automated decisioning

Gen's Martech organization is evolving from manually defined campaign and message-selection rules toward intelligent, learned decisioning. We're looking for a senior/staff-level Personalization & Decisioning Engineer to become the founding engineer for this transformation.

Responsibilities

Design, build, and operate a production-grade contextual bandit for message and campaign selection; Own the content-embedding pipeline that supports rapid generalization to new creative without requiring a full history of prior interactions; Build low-latency online serving systems and reliable feedback loops for impressions, selections, conversions, outcomes, and model learning; Establish evaluation, experimentation, monitoring, and rollback practices for safe production operation; Translate business and compliance requirements into durable system safeguards, including frequency caps, eligibility conditions, suppression rules, and disclosures; Make pragmatic modeling and architecture choices, using the simplest approach that meets business needs and earning complexity through measurable value; Mentor engineers and help shape the technical direction and roadmap for personalization and decisioning across Martech

Requirements

Significant experience designing and delivering production machine-learning or decisioning systems, typically at a senior or staff level; Demonstrated experience shipping a contextual bandit, recommender system, ranking system, or comparable online decisioning capability into production; Strong software engineering fundamentals and experience building reliable, observable services, real-time inference APIs, event-driven feedback systems, and production data pipelines; Practical knowledge of experimentation and online evaluation, including metrics, bias, exploration versus exploitation, and failure modes; Experience with embeddings, representation learning, content understanding, or systems that generalize to new items; Sound judgment about model complexity, operational risk, and when a simpler solution is the right one; Ability to communicate clearly with technical and non-technical stakeholders and align partners around trade-offs

Nice to have

Experience with personalization, lifecycle marketing, campaign optimization, recommender systems, or customer engagement platforms; Experience designing deterministic policy layers and guardrails around machine-learning systems; Familiarity with privacy, consumer protection, compliance, or regulated-product considerations in personalization; Experience setting technical direction for a new platform and mentoring engineers across teams