Remote Data Scientist - Mining Analytics

Placements24; Noord-Kaap; Hybrid; ZAR 900,000 - 1,800,000; Full time

Benefits offered by this job

Remote work stipend

Professional development budget

Competitive compensation

Job summary

Placements24 is seeking a highly analytical Remote Data Scientist specializing in mining analytics. This role uses advanced statistics and machine learning to extract actionable insights from large datasets related to exploration, production, and operations, working independently from a home office while collaborating virtually with geologists, engineers, and management.

You will develop models for predictive maintenance, resource estimation, and production forecasting; analyze data from

Qualifications

• Master's or PhD in Data Science, Statistics, Computer Science, or related quantitative field.
• Minimum of 5 years of experience as a Data Scientist, with a proven track record in predictive modeling and analytics.
• Proficiency in Python or R, and libraries such as Pandas, Scikit-learn, TensorFlow.
• Experience with big data technologies and cloud platforms (AWS, Azure, GCP).
• Strong understanding of statistical modeling, data mining, and ML algorithms.

Responsibilities

• Develop and deploy machine learning models for predictive maintenance, resource estimation, and production forecasting.
• Analyze large, complex datasets from geological surveys, sensor data, and operational logs.
• Create data visualizations and dashboards to communicate insights to stakeholders.
• Identify opportunities for process optimization and efficiency gains through data analysis.
• Stay abreast of the latest advancements in data science, AI, and mining applications.

Skills

Data science

Predictive modeling

Python

R

Pandas

Scikit-learn

TensorFlow

AWS

Azure

GCP

Big data

Education

MS/PhD in Data Science

MS/PhD in Statistics

MS/PhD in Computer Science

Tools

Pandas

Scikit-learn

TensorFlow

Job description

About the Role

Our client is seeking a highly analytical and innovative Remote Data Scientist specializing in mining analytics. This position leverages advanced statistical and machine learning techniques to extract actionable insights from vast datasets related to exploration, production, and operational efficiency. You will work independently from your home office, collaborating virtually with geologists, engineers, and management to identify trends, predict outcomes, and optimize mining processes. The ideal candidate possesses a strong quantitative background and experience applying data science methodologies to solve complex problems within the resource sector, contributing to data-driven decision-making across the organization.

Key Responsibilities

• Develop and deploy machine learning models for predictive maintenance, resource estimation, and production forecasting.


• Analyze large, complex datasets from various sources (e.g., geological surveys, sensor data, operational logs).


• Create compelling data visualizations and dashboards to communicate insights to technical and non-technical stakeholders.


• Identify opportunities for process optimization and efficiency gains through data analysis.


• Stay abreast of the latest advancements in data science, AI, and their applications in the mining industry.

Requirements

• Master's or PhD in Data Science, Statistics, Computer Science, or a related quantitative field.


• Minimum of 5 years of experience as a Data Scientist, with a proven track record in predictive modeling and analytics.


• Proficiency in programming languages such as Python or R, and relevant libraries (e.g., Pandas, Scikit-learn, TensorFlow).


• Experience with big data technologies and cloud platforms (e.g., AWS, Azure, GCP).


• Strong understanding of statistical modeling, data mining, and machine learning algorithms.

Benefits

• Highly competitive salary and performance-based bonus.


• Comprehensive health, dental, and vision insurance.


• Generous remote work stipend and professional development budget.


• Significant impact on strategic decision-making through data insights.


• A flexible and collaborative work environment with a focus on innovation.