About the role:

As a Senior ML Engineer, you’ll bring seniority and end-to-end fullstack data expertise to a data team on a mission to turn data into impact.

Over the past few months, our team has:

• Built a fraud detection model that flags suspicious transactions in real time and reduces manual review.

• Developed a non profit segmentation model to help marketing personalize campaigns and improve conversion rates.

• Experimented with a recommendation system to optimize donation tip suggestions and boost nonprofit revenue.

• Improved data reliability and monitoring across our stack with automated anomaly detection and alerts.

If you join us, you’ll take ownership of various projects that directly impact our growth. The role is very hands on and full stack, in a sense that you will :

• Maintain and improve our ingestion pipeline

• Discuss with our business stakeholders to translate business needs into data requirements

• Build models in dbt, build ML models and deploy them to production

• Communicate your results and monitor the business impact in Metabase or Streamlit

• Mentor the team to raise the bar for everyone at Zeffy

Data team principles at Zeffy

• Impact first: We optimize for maximum business impact with minimal effort, what we call Zeffy perfectionism. We have no problem shifting the priorities if we realize that the impact is higher or lower than expected. We are a small team of 2 and we need to be cautious about how we use our time to maximize our impact on the business

• Trust and ownership: Every project has a clear owner with full autonomy to deliver results : collaboration is encouraged, but accountability is clear. You are responsible of the project for the whole development cycle, from gathering the needs to designing the solution to implementing the solution to monitoring the impact

• Decentralized organization : Everyone at Zeffy is autonomous at monitoring the performance of their team with data and answer their basic questions. The role of the data team is to build the foundations for the teams to increase this level of autonomy. It comes with a clear and documented data model and scoring and classification projects to bring interpretability in our data

Our data Stack

• Languages & Frameworks: Python, SQL, scikit-learn, XGBoost, PyTorch / TensorFlow

• Data & MLOps: Snowflake, dbt, AWS, Metabase, Metaplane, Fivetran

We’re looking for someone with 4+ years of experience in ML engineering, data science, or applied data roles, ideally in a SaaS, fintech, or high-volume B2C/B2B tech environment.

You’ll fit right in if:

• You’ve deployed ML models into production and monitored their performance.

• You understand how to connect ML outputs to business decisions.

• You’re comfortable moving between data pipelines, APIs, and model code.

• You bring a software engineering mindset to ML (CI/CD, testing, version control).

• You’ve mentored or guided teammates on data and ML best practices.

• You can clearly communicate complex ML results to non-technical audiences and make insights actionable.

💡 Research shows that candidates from underrepresented backgrounds often don’t apply for roles if they don’t check every box. If this applies to you and you are interested in the position, we’d love to hear from you!

• Call with Recruiter (45min)

• Meeting with Hiring Manager (1.5h)

• Case Study (3h)

• Cultural interview with leadership (1h)

• Team Lunch / Reference check

• Job offer