AI & Data Science Expert

Project overview

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Data Scientist, AI Engineer

Mission Description

Our client, a leading European bank (anonymized for the test ad), wishes to accelerate its digital transformation by integrating high-impact business artificial intelligence solutions. As part of an ambitious program, we are looking for a senior freelance AI/Data Science professional to join a dedicated team and develop innovative models for fraud detection, credit scoring, and customer personalization.

This mission is part of an ethical and regulatory approach (GDPR, PSD2, Basel III), with access to massive and varied data (transactions, customer behaviors, regulatory texts).

In collaboration with the Data, Risk Management, and IT teams, you will be responsible for:

Model Design & Deployment:

Develop machine learning algorithms (supervised/unsupervised) for:

Real-time fraud detection (anomaly detection, graph networks).

Credit scoring optimization (hybrid models: XGBoost + deep learning).

Customer offer personalization (recommendation systems, NLP for feedback analysis).

Ensure model scalability and explainability (SHAP, LIME, tools like IBM AI Explainability 360).

Data Management & Compliance:

Work with sensitive data (PII) in compliance with GDPR and banking standards.

Collaborate with data engineers to optimize pipelines (Spark, Airflow, Databricks).

Document processes for audits (model cards, data lineage).

Industrialization & MLOps:

Deploy models in production (Docker, Kubernetes, AWS SageMaker/GCP Vertex AI).

Implement monitoring (drift detection, performance decay) using tools like MLflow or Evidently AI.

Automate model retraining (CI/CD for AI).

Business Collaboration:

Translate business needs into technical solutions (e.g., reduce false positives in fraud detection by 30%).

Train internal teams on tool usage (workshops, documentation).

Profile Sought

Mandatory Technical Skills:

Languages: Python (expertise in Pandas, NumPy, Scikit-learn), advanced SQL.

ML/DL Frameworks: TensorFlow/PyTorch, XGBoost, LightGBM, Hugging Face (for NLP).

MLOps: Experience with MLflow, Kubeflow, or cloud tools (AWS/GCP/Azure).

Big Data: Spark, Hadoop, or serverless tools (BigQuery, Snowflake).

Cloud: AWS/GCP/Azure certification (a plus).

Regulation: Knowledge of GDPR, PSD2, or Basel III issues (ideal but not mandatory).

Experience & Soft Skills:

5+ years of experience in Data Science/ML, including 2+ years in finance or banking.

Portfolio of concrete projects (GitHub, Kaggle, or anonymized case studies).

Ability to explain complex concepts to non-technical audiences.

Autonomy and rigor for remote work with international teams.

Fluent English (technical documentation, meetings).

Project details

• Start: ASAP
• Duration: 3 mois (renouvelables)
• Location: 75001 PARIS, FR
• Languages: French