Data Scientist - Latam only!

About the role

We're looking for a Data Scientist with strong machine learning expertise and practical mining industry experience to help build and improve predictive models used across mining and mineral processing operations. This is not a "train once and deploy" environment. You'll develop and maintain models that continuously adapt to changing geological conditions, ore variability, and operational differences across multiple mine sites. You'll work closely with domain experts and engineering teams to deliver measurable improvements in plant performance, recovery, and production outcomes. Mining operations often require ongoing model monitoring and adaptation because ore characteristics and process conditions evolve over time.

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
• Work with structured and unstructured industrial datasets to support production decision-making
• Design experiments and evaluate model performance in real operational environments
• Contribute to MLOps and model monitoring practices for production systems
• Required Qualifications
• 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
• Model monitoring and performance tracking
• Strong statistical modeling and experimentation skills
• Experience working with large operational or industrial datasets
• Excellent communication skills and ability to collaborate with cross-functional teams
• Strongly Preferred
• Direct experience in mining, mineral processing, metallurgy, or mine-to-mill optimization
• Understanding of:
• Ore variability
• Rock hardness and fragmentation
• Flotation processes
• Recovery optimization
• Mill performance drivers
• Production process analytics
• Experience supporting multiple operational sites with varying geological conditions
• Experience with time-series modeling and industrial process optimization

Requirements

• Experience with MLOps frameworks
• Knowledge of process control systems and industrial data platforms
• Experience with predictive maintenance or optimization systems
• Background in copper, gold, or base metals operations
• Compensation & Benefits
• Competitive compensation (~USD $140,000/year, depending on experience)
• Fully remote
• Opportunity to work on cutting-edge AI applications in the mining industry
• Small, highly technical team with direct impact on product and customer outcomes
• Fast-moving hiring process
• Interview Process
• G2i recorded interview (experience review + targeted technical deep dive)
• Client interview with VP of Data Science
• Final decision
• We're especially interested in candidates based in South America, with Chile being a particularly strong market due to the concentration of advanced mining operations in the region.

Benefits

Additional Information

Data Scientist (Machine Learning for Mine-to-Mill Optimization) Remote | South America Preferred (Chile, Peru, Brazil, Argentina) | Direct Hire We're partnering with an innovative AI company transforming mining operations through machine learning and advanced analytics. Their platform helps mining companies optimize the entire mine-to-mill process, improving recovery, throughput, and operational efficiency through data-driven decision making. The company specializes in applying AI to real-world mining challenges and building production-grade models that continuously evolve as operating conditions change.

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