Data Scientist - Latam only!
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.
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. What You'll Do
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
Nice to Have
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.