Remote Lead Data Scientist - Predictive Analytics (Mining)

Placements24; Cape Town; Hybrid; ZAR 1,600,000 - 2,200,000; Full time

Benefits offered by this job

Fully remote

Competitive salary

Health coverage

Annual leave

Professional development budget

Conferences

Job summary

Placements24 seeks a visionary Remote Lead Data Scientist to guide a distributed team in a global mining analytics program. You will design and deploy scalable predictive models for ore-grade prediction, maintenance, and production forecasting, shaping data strategy and MLOps in a fully remote environment.

This role requires strong leadership, deep statistical knowledge, and the ability to translate complex results into actionable insights for stakeholders worldwide.

Qualifications

• Master's or PhD in a quantitative field.
• 7+ years in data science with 2+ years in leadership.
• Experience applying predictive modeling to large-scale datasets in mining or related sectors.
• Proficiency in Python or R and ML libraries (TensorFlow, PyTorch, Scikit-learn).
• Strong cloud experience (AWS/Azure/GCP) and distributed computing (Spark).
• Experience with data visualization tools (Tableau, Power BI, Matplotlib).
• Excellent communication and remote leadership capabilities.

Responsibilities

• Lead design and deployment of advanced ML models for predictive analytics in mining contexts.
• Mentor a remote team of data scientists and engineers; provide technical guidance.
• Collaborate with domain experts to translate business needs into data projects.
• Develop robust data pipelines and feature engineering strategies.
• Evaluate and select statistical methods for mining challenges.
• Communicate findings clearly to technical and non-technical stakeholders.
• Promote best practices in data science, MLOps, and reproducible research.
• Stay updated with cutting-edge AI technologies relevant to mining.

Skills

Remote team leadership

Data science leadership

Python

R

TensorFlow

PyTorch

Scikit-learn

Pandas

NumPy

Spark

Tableau

Power BI

Education

Master's degree in Data Science

PhD in related quantitative field

Tools

Python

R

TensorFlow

PyTorch

Scikit-learn

Pandas

NumPy

Tableau

Power BI

Spark

Job description

About the Role

Our client, a global leader in the mining industry, is revolutionizing its operations through advanced data analytics and is seeking a visionary Remote Lead Data Scientist. This is a fully remote position, offering the flexibility to work from anywhere while playing a critical role in developing and deploying sophisticated predictive models. You will spearhead initiatives to extract actionable insights from vast datasets, driving optimization in areas such as resource exploration, production forecasting, equipment maintenance, and safety monitoring across various mining operations worldwide. This role demands a strategic thinker with exceptional technical acumen and the ability to lead a distributed team of data scientists and engineers. You will be at the forefront of leveraging AI and machine learning to transform the future of mining, ensuring our client remains competitive and innovative in the digital age. The focus is on building scalable, robust solutions that deliver tangible business value, making this an exciting opportunity for a seasoned professional seeking impact and autonomy.

Key Responsibilities

• Lead the design, development, and implementation of advanced machine learning models and algorithms for predictive analytics within the mining context, focusing on areas like ore grade prediction, equipment failure forecasting, and operational efficiency.
• Manage and mentor a remote team of data scientists and engineers, providing technical guidance, code reviews, and fostering collaboration.
• Collaborate with domain experts, geologists, and operations managers to understand business needs and translate them into data science projects.
• Develop and maintain robust data pipelines and feature engineering strategies to ensure high-quality input for models.
• Evaluate and select appropriate statistical methodologies and machine learning techniques for specific mining challenges.
• Communicate complex findings and model results clearly and effectively to both technical and non-technical stakeholders through compelling visualizations and presentations.
• Champion best practices in data science, MLOps, and reproducible research within the organization.
• Stay current with cutting‑edge research and technologies in data science, AI, and their applications in the natural resources sector.

Requirements

• Master's or Ph.D. in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field.
• A minimum of 7 years of hands‑on experience in data science and machine learning, with at least 2 years in a leadership or senior capacity.
• Demonstrated experience in applying predictive modeling techniques to complex, large‑scale datasets, preferably within the mining or related heavy industry sectors.
• Proficiency in programming languages such as Python or R, and expertise in libraries like TensorFlow, PyTorch, Scikit‑learn, Pandas, and NumPy.
• Strong experience with cloud platforms (AWS, Azure, GCP) and distributed computing frameworks (e.g., Spark).
• Solid understanding of statistical modeling, experimental design, and data mining techniques.
• Experience with data visualization tools (e.g., Tableau, Power BI, Matplotlib).
• Excellent problem‑solving, critical thinking, and communication skills, especially in a remote work setting.
• Proven ability to manage projects, deadlines, and remote teams effectively.

Benefits

• Fully remote work arrangement with the flexibility to work from anywhere.
• Highly competitive salary and performance-based bonus structure.
• Comprehensive benefits package including health, dental, and vision coverage.
• Generous annual leave and public holiday allowance.
• Professional development budget for courses, conferences, and certifications.
• Opportunity to work with cutting‑edge technologies and make a significant impact on a global industry.
• Access to a collaborative online community of peers and experts.