Data Scientist - Machine Learning for Mine-to-Mill Optimization - Remote

4w1 month ago

G2i Inc.

Remote · Full-time · $140,000 – $140,000

About this role

This role focuses on building and improving predictive models used across mining and mineral processing operations, specifically for mine-to-mill optimization. You will help mining companies optimize their entire process, improving recovery, throughput, and operational efficiency through data-driven decision making.

In this position, you will develop and maintain models that continuously adapt to changing geological conditions, ore variability, and operational differences across multiple mine sites. You will analyze large-scale mining and processing datasets to identify operational improvement opportunities and design experiments to evaluate model performance in real operational environments.

You will work closely with mining engineers, metallurgists, and operations teams to translate business challenges into ML solutions. The environment is collaborative and hands-on, requiring you to partner with domain experts to deliver measurable improvements in plant performance and production outcomes.

This role offers the opportunity to contribute to MLOps and model monitoring practices for production systems. You will work with both structured and unstructured industrial datasets to support production decision-making, with models that continuously evolve as operating conditions change.

Requirements

5+ years of experience in Data Science, Machine Learning, or Applied AI
Strong Python and machine learning fundamentals
Experience building production ML systems and maintaining models over time
Hands-on experience with Google Cloud Platform (GCP), BigQuery, Parquet-based data pipelines, and model monitoring
Strong statistical modeling and experimentation skills
Experience working with large operational or industrial datasets
Direct experience in mining, mineral processing, metallurgy, or mine-to-mill optimization

Responsibilities

Build, deploy, and improve machine learning models for mine-to-mill optimization
Analyze large-scale mining and processing datasets to identify operational improvement opportunities
Develop predictive models related to ore characteristics, fragmentation, recovery, flotation, throughput, and plant performance
Monitor model performance and address model drift across sites and changing geological conditions
Partner with mining engineers, metallurgists, and operations teams to translate business challenges into ML solutions
Design experiments and evaluate model performance in real operational environments
Contribute to MLOps and model monitoring practices for production systems

Benefits

Remote work flexibility with a preference for South America (Chile, Peru, Brazil, Argentina)
Direct hire opportunity with an innovative AI company transforming mining operations
Work on production-grade models that continuously evolve with real-world mining conditions