Data Scientist, Mine-to-Mill Optimization

The Data Scientist, Mine-to-Mill Optimization role at KGHM International Ltd. focuses on developing and improving predictive, prescriptive, and machine learning models that support ore tracking, blending decisions, plant recommendations, and impact measurement. This hybrid position is based at Robinson Mine in Ely, Nevada, with regular on-site collaboration expected during the initial stages.

Key Responsibilities

• Prepare analytical datasets and perform exploratory analysis of geological, fleet, stockpile, laboratory, and plant-process data.
• Build and validate machine learning models for throughput, recovery, quality, and other agreed operational outcomes.
• Develop feature-engineering logic, segmentation, anomaly treatment, and model explanations.
• Support optimization models and what-if scenarios using operational constraints and site priorities.
• Run offline tests, passive tests, and controlled production trials with operations and metallurgy teams.
• Monitor model accuracy, recommendation acceptance, realized impact, and changing process conditions.
• Investigate performance deterioration and implement approved model improvements.
• Create clear analytical documentation and train KGHMI users and technical support staff.
• Perform other duties as assigned.

Modeling and Collaboration Scope

Work includes building and validating ML models, supporting optimization and decision-support scenarios, and maintaining model performance as operating conditions and ore characteristics change. The role also emphasizes collaboration with engineers, metallurgists, and operators to translate domain knowledge into model features and constraints.

Required Qualifications

• Degree in data science, engineering, statistics, applied mathematics, computer science, or a related field.
• 7+ years of experience in a similar Data Scientist role (Mining industry experience required).
• Hands-on experience with Python and SQL, including supervised learning, time-series analysis, and model evaluation.
• Understanding of data leakage, bias, confidence intervals, baselines, and causal or quasi-experimental impact measurement.
• Ability to work directly with engineers, metallurgists, and operators and convert domain knowledge into model features and constraints.
• Practical experience using source control, reproducible pipelines, and model-tracking tools.

Preferred Skills

• Experience with industrial process data, sensor data, optimization, or operational decision-support systems.

Technology Stack

Python and SQL.

Work Location and Work Style

Hybrid arrangement based in Salt Lake City, UT 84116, with work associated to Robinson Mine in Ely, Nevada and an expectation of regular on-site collaboration, especially during the initial stages.

Compensation and Employment Details

• Salary range: USD 140,000 - 170,000 per year
• Full-time position

Benefits

• Market-aligned base salary
• Performance-based Incentive awards
• Health, dental, and vision coverage
• Life and disability insurance
• Employee Assistance Program (EAP) for mental health and personal support
• RRSP matching
• Generous paid time off and holidays
• Flexible work arrangements (hybrid/remote options)
• Free on-site parking
• Casual Dress
• Tuition reimbursement for continuous learning
• On job training and support
• Opportunities for global mobility
• Respectful and responsible work environment
• Culture of safety, integrity, and sustainability
• Values that support teamwork and collaboration
• Flexible schedule
• Life insurance
• Paid time off
• Tuition reimbursement
• Vision insurance
• Dental insurance
• Employee assistance program

Application Screening Question

• How many years of experience do you have working in the Mining Industry?

Experience Level

• Data Scientist: 7 years (Preferred)