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 (e.g., Pandas, NumPy). • Proficiency in data manipulation, cleaning, preprocessing and feature engineering … • Proficiency in deep learning frameworks (e.g. Keras, PyTorch). • Theoretical and practical knowledge of popular machine learning algorithms (e.g., PCA, Support Vector Machines, RandomForest, XGBoost, skforecast). • Theoretical and practical knowledge of popular optimization methodologies (ex. PSO, GA, SGD…). • Experience with common development tools (e.g., PyCharm, Jupyter, Docker, Git). • Excellent communication skills, both verbal and written. Profile / Requirements : BSc or MSc degree in a relevant field (e.g., 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 (e.g., Pandas, NumPy). • Proficiency in data manipulation, cleaning, preprocessing and feature engineering … • Proficiency in deep learning frameworks (e.g. Keras, PyTorch). • Theoretical and practical knowledge of popular machine learning algorithms (e.g., PCA, Support Vector Machines, RandomForest, XGBoost, skforecast). • Theoretical and practical knowledge of popular optimization methodologies (ex. PSO, GA, SGD…). • Experience with common development tools (e.g., PyCharm, Jupyter, Docker, Git). • Excellent communication skills, both verbal and written.