Data scientist-tabular data

Responsibilities

• Understanding business objectives and developing AI solutions that help to achieve them, along with

metrics to track their progress.

• Prepare, clean, and preprocess data for analysis.
• Analyze data quality and proactively address issues.
• Develop data-driven algorithms for clustering, classification, regression, and optimization.
• Evaluate AI solutions aligned with business objectives.
• Deploy and manage AI models in production.
• Identify differences in data distribution that could potentially affect model performance in real-world

applications.

• Analyzing the errors of AI models and designing strategies to overcome them.
• Maintain and enhance existing solutions to meet evolving business needs.
• Visualize and communicate results analysis effectively.
• Present ideas, plans, and findings orally and in written reports.
• Collaborate with data scientists, data engineers, and software engineers on production applications.

Experience

• 5+ years of experience demonstrating depth and breadth in state-of-the-art machine-learning, deep

learning and optimization.

• Demonstrated experience in developing core AI algorithms in industry or for real-world problems.
• Proven track record of implementing robust and scalable industrial AI solutions.
• Strong understanding of the unique challenges and complexities involved in optimization.
• Experience in implementation of MLOps pipelines is a plus.
• Experience in the Oil & Gas industry is a plus.

Key Skills

• Strong background in applied mathematics, algorithms, and coding.
• Proficiency in statistics, machine learning, and deep learning.
• Proficiency in Python programming and data analysis libraries (eg Pandas, NumPy) .
• Proficiency in data manipulation, cleaning, preprocessing and feature engineering .
• Proficiency in deep learning frameworks (eg Keras, PyTorch) .
• Theoretical and practical knowledge of popular machine learning algorithms (eg PCA, Support Vector

Machines, RandomForest, XGBoost, skforecast) .

• Theoretical and practical knowledge of popular optimization methodologies (ex. PSO, GA, SGD.) .
• Experience with common development tools (eg PyCharm, Jupyter, Docker, Git) .
• Excellent communication skills, both verbal and written.

Profil recherché

BSc or MSc degree in a relevant field (eg Computer Science, Statistics) . PhD degree is a plus.

Key Skills

• Strong background in applied mathematics, algorithms, and coding.
• Proficiency in statistics, machine learning, and deep learning.
• Proficiency in Python programming and data analysis libraries (eg Pandas, NumPy) .
• Proficiency in data manipulation, cleaning, preprocessing and feature engineering .
• Proficiency in deep learning frameworks (eg Keras, PyTorch) .
• Theoretical and practical knowledge of popular machine learning algorithms (eg PCA, Support Vector

Machines, RandomForest, XGBoost, skforecast) .

• Theoretical and practical knowledge of popular optimization methodologies (ex. PSO, GA, SGD.) .
• Experience with common development tools (eg PyCharm, Jupyter, Docker, Git) .
• Excellent communication skills, both verbal and written.