Applied ML Scientist: Revenue Impact & Production Systems

Zendesk, Inc.; Portugal; Hybrid; EUR 60,000 - 90,000; Full time

Job summary

Zendesk, Inc. is seeking an Applied Machine Learning Scientist to build models that predict revenue-related outcomes and surface actionable insights.

You will train, validate, and deploy end-to-end ML solutions in collaboration with AI/ML Engineers, Data Engineers, and Analysts within Enterprise Data & Analytics. You will own the modeling lifecycle—from problem formulation to deployment and monitoring—delivering measurable business impact while handling both structured and unstructured data and

Qualifications

• 3–5 years in Applied ML, Data Science or related quantitative field with measurable business impact.
• Experience training models on messy real-world data with label noise, data drift and robust validation.
• Experience applying ML to structured and unstructured data including NLP, embeddings, LLMs or deep learning.
• Production-grade engineering skills: Python, SQL, software engineering best practices, deployment and monitoring of ML models.

Responsibilities

• Design, train, evaluate, and deploy ML models predicting/revealing revenue outcomes (churn, expansion, conversion, engagement).
• Build end-to-end ML systems from data prep to production deployment and monitoring.
• Develop insights from structured and unstructured data using statistical modelling, NLP, embeddings, LLMs, and deep learning.
• Design experiments to link customer behavior and product usage to business outcomes, translating model outputs into actions.
• Collaborate with Product, Engineering, and stakeholders to identify high-impact ML opportunities.
• Monitor model performance, detect drift, validate outcomes, and iterate based on real-world feedback.

Skills

Python

SQL

ML deployment

Experiment design

Data analysis

NLP

LLMs

Deep learning

Statistics

Product mindset

Tools

Docker

ML pipelines

Job description

Zendesk, Inc. is seeking an Applied Machine Learning Scientist to build models that predict revenue-related outcomes and surface actionable insights.

You will train, validate, and deploy end-to-end ML solutions in collaboration with AI/ML Engineers, Data Engineers, and Analysts within Enterprise Data & Analytics. You will own the modeling lifecycle—from problem formulation to deployment and monitoring—delivering measurable business impact while handling both structured and unstructured data and