Commercial Data Scientist

This is a fully remote position, open to applicants in Europe.

📋 Description

• Collaborate with Sales, RevOps, Customer Success, and Marketing to convert ambiguous commercial inquiries into quantifiable problems and datasets ready for modeling.

• Develop and refine predictive and classification models (such as health scoring and intent scoring), ensuring rigorous validation, monitoring, and well-defined success metrics.

• Implement models into production alongside Data Engineering, handling batch jobs, pipelines, feature generation, versioning, and observability.

• Sustain and enhance existing models through performance monitoring, retraining strategies, drift detection, and ensuring reliability.

• Ensure models are user-friendly by providing clear outputs, comprehensive documentation, and guidance for commercial teams to leverage insights.

• Foster a strong data science craft culture emphasizing code quality, reproducibility, disciplined experimentation, and pragmatic model selection.

⛳️ Requirements

• Several years of professional experience as a Data Scientist (or a related role), developing statistical and machine learning models from start to finish.

• Solid foundations in applied machine learning and statistics, with the ability to make sound judgments regarding model complexity versus impact.

• A production-oriented mindset: you have experience with deployed models and understand the importance of monitoring, retraining, data quality, and operational constraints.

• Proficient in SQL and Python, with a background in data wrangling and feature engineering.

• Capable of clearly communicating with both technical and non-technical stakeholders, including the ability to elucidate trade-offs and model limitations.

• Comfortable working in a high-autonomy setting: you can effectively plan your tasks, align with others, and deliver results without needing explicit assignments.

• Experience addressing commercial or go-to-market challenges (such as revenue intelligence, lead scoring, churn analysis, expansion, attribution, and forecasting).

• Familiarity with modern data stacks (like Snowflake, dbt, and Airflow) and production machine learning practices.

• Experience designing model outputs that seamlessly integrate into commercial workflows (like dashboards, alerts, and CRM signals).

🏝️ Benefits

• Work on challenges that lie at the intersection of product usage and commercial success.

• Take ownership of significant, end-to-end projects—from definition to production.

• Join a team that prioritizes autonomy, craftsmanship, and speed.