Join a team at the heart of ML innovation !

The Knowledge team is responsible for developing ML algorithms and features around transaction enrichment and user knowledge.

It combines real-time NLP ML model inference and batch processing for marketing targeting, at-scale on large volumes of transactional data in our data warehouse.

As a Data Scientist within the Knowledge team, you will:

• Develop, enhance, and deploy machine learning models for banking transaction contextualization with a focus on accuracy, performance, and scalability

• Work at the intersection of data science, MLOps and research to design advanced NLP models, recommendation systems, and real-time inference systems

• Build and maintain our ML model training pipelines, feature engineering workflows, and model monitoring systems

• Create tools and automation for model evaluation, A/B testing, and performance tracking

• Develop technical frameworks for ML experimentation, model versioning, and deployment

• Actively contribute to model quality, data quality, and research reproducibility

Technical skills

• Machine learning: 2-3 years of professional experience (excluding studies, internships, and apprenticeships)

• Python: confirmed level (main language)

• Deep Learning frameworks (TensorFlow/PyTorch): confirmed level

• Data manipulation (Pandas/Polars): beginner to confirmed

• SQL (PostgreSQL or other relational DBMS): beginner to confirmed

• MLOps tools and practices: beginner level

• Git/Linux/Docker: confirmed level

Mindset

• Autonomous and able to take initiative

• Research-oriented with an appetite for experimentation and innovation

• Team player who enjoys collaborating and sharing, and works towards collective success

• Builder rather than power-user: you enjoy creating and filling gaps in a growing scale-up ecosystem

• Open source contributions or personal projects are welcome!

Here’s our tech stack

Experience in these areas can make the difference:

• Python / PyTorch

• Polars / Pandas

• PostgreSQL

• RabbitMQ / Kafka

• GitLab

• Clickhouse

• Dagster

• dbt

• MLflow / Weights & Biases

• Initial discussion with the CTO and Head of Data (30 min)

• Technical test assignment

• Technical discussion around the test with the team (1h30)

• Final interview (30min)

Benefits

• Attractive compensation package!

• Meal vouchers (Swile card)

• Sustainable Mobility allowance combinable with Navigo Pass reimbursement

• “Remote friendly” work environment (possibility to work from home 2 days per week)

• Offices in central Paris, 9th district

• Participation in tech events (internal or external)

• Support for publishing technical content

• Bi-monthly All hands meetings in Paris followed by an evening event

• Annual multi-day company offsite

• Opportunity to participate in Paylead hiking trips