Applied ML Scientist

Zendesk, Inc.; Lisboa; Híbrido; EUR 60 000 - 90 000; Tempo integral

Resumo da oferta

Zendesk, Inc. is seeking an Applied ML Scientist to build a customer intelligence layer that links signals to revenue outcomes. You will train models, run experiments, and deploy production systems that drive daily business decisions.

You will own end-to-end solutions and collaborate with AI/ML Engineers, Data Engineers, and Analysts in Enterprise Data & Analytics. You will work with structured data, such as usage metrics and revenue events, and unstructured data like tickets and feedback,

Qualificações

• Strong Python programming with production-grade code and tests.
• Experience with SQL and cloud data warehouses.
• Experience deploying and monitoring models in production.

Responsabilidades

• Train, evaluate, and deploy models predicting revenue outcomes.
• Design experiments to link customer signals with business results.
• Build production ML pipelines for ingestion, retraining, validation, and serving.
• Work with structured and unstructured data (usage metrics, tickets, feedback).
• Utilize LLMs and deep learning where appropriate (embeddings, fine-tuning).
• Define and track model performance in production and monitor drift.
• Own models end-to-end from prototype to deployment and maintenance.
• Collaborate with product and engineering to embed intelligence into decision points.
• Translate model outputs into actionable insights for sales, success, and product teams.

Conhecimentos

Python

SQL

ML modeling

Experiment design

NLP embeddings

Production deployment

Formação académica

BA/BS in CS/Statistics/Math

Advanced degree optional

Ferramentas

Snowflake

Airflow/dbt

Descrição da oferta de emprego

Role Overview

We've spent the past year consolidating and validating our revenue data, most signals now live in one place. The next step is building a customer intelligence layer: systems that dynamically learn which signals drive outcomes, adapt as the business evolves, and surface insights that change how we act. As an Applied ML Scientist, you will be the person who makes that happen. You will train models, design experiments, and uncover the patterns that connect customer behavior to revenue outcomes. Then ship those insights as production systems that the business relies on daily. You own problems end-to-end: from formulating the right question, to training and validating models, to deploying them and measuring whether they actually moved the needle, in a closed feedback loop. You will work alongside a team of AI/ML Engineers, Data Engineers, and Analysts as part of the Enterprise Data & Analytics department. Your focus is the science: understanding what drives outcomes, building models that learn from data, and turning that understanding into systems that work.

Key Responsibilities

• Train, evaluate, and deploy models that predict and explain revenue-related outcomes (churn, expansion, conversion, engagement)
• Design and run experiments to establish relationships between customer signals and business results
• Build production ML pipelines that learn continuously: ingest new data, retrain, validate, and serve predictions at scale
• Work with large volumes of both structured data (usage metrics, revenue events, account attributes) and unstructured data (support tickets, conversations, product feedback)
• Use LLMs and deep learning where they're the right tool (embeddings, fine-tuning, text feature extraction)
• Define and track model performance in production: monitor drift, measure business impact, and iterate when models degrade
• Own your models end-to-end from prototype through production deployment, monitoring, and maintenance
• Partner with product and engineering to embed intelligence where users make decisions
• Translate model outputs into actionable insights for sales, success, and product teams: make the intelligence layer useful, not just accurate
• Collaborate with stakeholders to identify high-leverage questions and prioritize modeling work based on expected business impact

This Role Is For You

You get more satisfaction from a simple model that ships and moves a metric than a complex one that scores well on a holdout set. You've trained models on real-world messy data and dealt with the unglamorous parts: label noise, class imbalance, feature leakage, data drift. You write production Python — tests, type hints, clean abstractions — not just notebooks with df_final_v3. You think about problems in terms of "what will someone do differently because of this?" rather than "what's the most sophisticated technique?" You care that users actually change behavior because of your work: you're not done when the model is accurate, you're done when someone acts differently. You form opinions about what to build next based on data and user understanding, not just what's assigned to you

Education & Experience

• 3–5 years' experience in applied machine learning, data science, or a related field
• BA/BS in Computer Science, Statistics, Mathematics, or related quantitative discipline
• Advanced degrees welcome but not required

Technical Expertise

• Strong foundations in statistical modeling and machine learning: regression, classification, survival analysis, causal inference, uplift modeling, or similar
• Experience training models on real-world datasets: feature engineering, validation strategy, handling messy data at scale
• Comfort working with unstructured data (text, conversations) using embeddings, fine-tuning, or learned representations: you understand LLMs as modeling tools, not just API endpoints
• Strong Python programming skills: you write production-grade code with tests, not just scripts
• Strong SQL skills and experience with cloud data warehouses (Snowflake preferred)
• Experience deploying and monitoring models in production (batch or real-time)
• Nice-to-have: Experience with experiment design, A/B testing, and causal inference
• Nice-to-have: Experience with orchestration tools (Airflow, dbt, or similar)
• Nice-to-have: Experience with AI-assisted development workflows (Claude Code, Cursor, Copilot, or similar)

Company Overview

Zendesk software was built to bring a sense of calm to the chaotic world of customer service. Today we power billions of conversations with brands you know and love. Zendesk believes in offering our people a fulfilling and inclusive experience. Our hybrid way of working, enables us to purposefully come together in person, at one of our many Zendesk offices around the world, to connect, collaborate and learn whilst also giving our people the flexibility to work remotely for part of the week. As part of our commitment to fairness and transparency, we inform all applicants that artificial intelligence (AI) or automated decision systems may be used to screen or evaluate applications for this position, in accordance with Company guidelines and applicable law. Zendesk is an equal opportunity employer, and we’re proud of our ongoing efforts to foster global diversity, equity, & inclusion in the workplace. Individuals seeking employment and employees at Zendesk are considered without regard to race, color, religion, national origin, age, sex, gender, gender identity, gender expression, sexual orientation, marital status, medical condition, ancestry, disability, military or veteran status, or any other characteristic protected by applicable law. We are an AA/EEO/Veterans/Disabled employer. If you are based in the United States and would like more information about your EEO rights under the law, please click here. Zendesk endeavors to make reasonable accommodations for applicants with disabilities and disabled veterans pursuant to applicable federal and state law. If you are an individual with a disability and require a reasonable accommodation to submit this application, complete any pre‑employment testing, or otherwise participate in the employee selection process, please send an e‑mail to [e-mail] with your specific accommodation request. Zendesk is on a mission to simplify the complexity of business and make it easy for companies and customers to create connections. Our customer experience software unlocks the power of billions of interactions, enabling businesses to build rich, meaningful relationships with their customers. What’s it like to work here? Our offices reflect the global cities we call home, and have spaces for collaboration, quiet, and events. With our hybrid approach, you’ll experience flexibility and connection, collaboration, and learning with your team.