Applied Machine Learning Engineer

About Bagama Mining

Bagama Mining is a small-scale gold producer operating in the southwestern region of Mali, near Bamako. Committed to sustainable mining practices, the company upholds strict environmental and social management plans (ESMP) to minimize its ecological impact. Bagama Mining prioritizes safety, health, and close cooperation with local communities to foster positive relationships and responsible resource extraction. The company values innovation and seeks to integrate advanced technologies in its operations.

Role Description

This is a full-time hybrid role for an Applied Machine Learning Engineer based in Paris, with flexibility for some remote work. The role involves designing, building, and deploying machine learning models to optimize mining operations and processes. Responsibilities include data collection, preprocessing, model development, evaluation, and deployment, as well as collaborating with cross-functional teams to enhance decision-making and operational efficiency. The engineer will also focus on the continuous improvement of existing models and contribute to research and development initiatives.

Qualifications

• Experience with Applied Machine Learning, including developing, testing, and deploying predictive models using algorithms.
• Strong knowledge of Data Preprocessing, Feature Engineering, and Machine Learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Programming skills in Python, R, or a similar language, with a focus on data science and machine learning applications.
• Proficiency in Data Analytics, Statistical Methods, and Data Visualization techniques.
• Experience with Big Data tools such as Spark, Hadoop, or cloud-based machine learning platforms.
• Familiarity with mining industry operations, predictive maintenance models, and geospatial data analysis is a plus.
• Strong problem-solving, critical-thinking, and communication skills.
• Master's or Ph.D. in Computer Science, Data Science, Artificial Intelligence, or a related field.