Data scientist/ML Engineer: Personalization and automated decisioning
gen-digital · CZE - Prague ·
- Work mode
- Hybrid
- Seniority
- Senior
- Category
- Data science
gen-digital · CZE - Prague ·
Gen is a global leader dedicated to powering Digital Freedom through trusted consumer brands, including Norton, Avast, and LifeLock. Our products help people live their digital lives safely, privately, and confidently.
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.
You’ll build and own the systems that decide which message or creative to deliver, to whom, and when. This includes a contextual-bandit-based message-selection platform, a content-embedding pipeline that enables new creative to generalize immediately, and the low-latency serving and feedback infrastructure that connects these capabilities to production experiences.
You’ll partner with product, marketing, data science, engineering, business, and compliance stakeholders to improve relevance and outcomes while ensuring safeguards—including frequency caps, eligibility rules, customer protections, and required disclosures—remain enforced above the models.
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.
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.
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.
Significant experience designing and delivering production machine-learning or decisioning systems, typically at a senior or staff level. ML frameworks experience (TensorFlow, PyTorch, ....)
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.
Software development experience (Python, Java, etc)
Experience with personalization, lifecycle marketing, campaign optimization, recommender systems, or customer engagement platforms.
Experience shipping a contextual bandit, recommender system, ranking system, or comparable online decisioning capability into production is a plus.
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.
You’ll help define how Gen connects people with the right product, message, and experience at the right time. Your work will turn personalization from a collection of static rules into an intelligent, measurable, and responsibly governed decisioning capability that can scale across Gen’s brands and customer journeys.
Gen is committed to building an inclusive workplace where diverse perspectives are valued and every person can do their best work.