Senior Data Scientist: Causal Inference & Production ML

Bitvavo; Amsterdam; On-site; EUR 90,000 - 130,000; Full time

Job summary

Bitvavo is seeking a data-focused professional to lead causal inference projects that guide spend, pricing, product decisions and financial integrity. You will build and productionize models, monitor them, and communicate insights to senior stakeholders while driving ownership and accountability.

The role emphasizes strong Python/SQL skills, experience with data platforms, and a track record of deploying models that influenced real decisions. Collaboration and initiative are valued.

Qualifications

• Strong applied experience turning a messy business question into a model or a measurement.
• Fluency in Python and SQL, and comfort working independently.
• Track record of models that made it into production and changed a decision.
• Sound statistical judgement and ability to quantify uncertainty.
• Excellent communication skills to derive requirements and drive decisions.

Responsibilities

• Own problems end to end: frame the question with stakeholders, build the model, ship it, and productionize it.
• Work backwards from the decision—focus on decision impact and cost of wrong choices.
• Monitor models in production: drift, retraining, and re-evaluation.
• Communicate insights to senior stakeholders, even when results are not favorable.
• Support teammates by setting standards for causal inference and knowledge sharing.

Skills

Causal inference

Python

SQL

Statistical judgement

Communication

Ownership

Agent frameworks (nice to have)

Automation platforms (n8n)

Tools

Snowflake

dbt

Airflow

Looker

MySQL

Druid

Kafka

Flink

Job description

Bitvavo is seeking a data-focused professional to lead causal inference projects that guide spend, pricing, product decisions and financial integrity. You will build and productionize models, monitor them, and communicate insights to senior stakeholders while driving ownership and accountability.

The role emphasizes strong Python/SQL skills, experience with data platforms, and a track record of deploying models that influenced real decisions. Collaboration and initiative are valued.