Data Scientist - Latam only!

Data Scientist (Machine Learning for Mine-to-Mill Optimization)

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### 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.