TECHNICO-COMMERCIAL ITINÉRANT PRODUITS BIOSTIMULANTS F / H

SOFRAPAR
Flévy
EUR 50 000 - 90 000
Description du poste

Job Description : 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 (e.g. 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.

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