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# Data Scientist

- **Company:** BNP Paribas
- **Location:** Lisbon, OH, United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Cluster Analysis, Computer Programming, Data Cleansing, Extract Transform Load (ETL), Data Security, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, Natural Language Processing, NumPy, Tensorflow, Pytorch, Keras, Pandas, Scikit Learn, Information Technology, Data Analytics
- **Published:** September 5, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/p1d1suzhia

## About the Role

* Master's or PhD in Mathematics, Computer Science, Data Science or a related quantitative field
 * Strong foundation in Probability, Inference and Linear Algebra
 * Knowledge or experience with Machine-Learning techniques such as Classification, Prediction and Clustering
 * Programming skills in Python and knowledge of common numerical and machine-learning packages, such as NumPy, scikit-learn, pandas, Keras, TensorFlow and PyTorch
 * Ability to write clear and concise code
 * Knowledge of data analytics, manipulation and visualization techniques
 * Intellectually curious and willing to learn challenging concepts on a daily basis

## Description

The Global Markets Data (GM) and AI Lab is a team of data scientists specializing in Machine Learning, Artificial Intelligence, Data Modelling and Natural Language Processing, to deliver high impact business solutions and to drive innovation in data-driven decision-making.

Based in Lisbon, you'll work within a team of finance professionals from our global hubs in London, Paris, New York, etc., supporting BNPP CIB market activities globally, with a primary focus on Front Office Control and Surveillance (FOCS) topics. You'll develop advanced analytical and machine-learning solutions to improve surveillance of communications, helping detect and prevent undesirable behavior, meaning unprofessional conduct and market abuse

Your Main Activities Are

 * Explore and examine data from diverse data sources
 * Conceptual modelling, statistical analysis, predictive modelling and optimization design
 * Understand and work around limitations in analytic models
 * Data cleanup, normalization and transformation
 * Process optimization design based on historical data
 * Hypothesis testing: being able to develop hypothesis and test with careful experiments
 * Discover hidden insights/embedded patterns and enable business stakeholders to make decisions that are more informed
 * Help build workflows for extraction, transformation and loading of different data from a variety of sources and enable linking them to existing systems and datasets
 * Ensure the integrity and security of data


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