Data Scientist - Tabular Data

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.

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., 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.