Data Scientist - Tabular Data H/F

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.