Remote Senior Data Scientist - Mining Analytics

Placements24; Mbombela (Nelspruit); Hybrid; ZAR 1,000,000 - 1,600,000; Full time

Benefits offered by this job

Remote work in SA

Competitive salary

Professional development budget

State-of-the-art remote setup

Job summary

Placements24 is seeking a highly skilled Senior Data Scientist to join our analytics team. This role is remote within South Africa and focuses on mining operations, predictive maintenance, and resource optimization.

You will collaborate with geologists, engineers and operations managers to deliver data-driven insights, build advanced models, and develop scalable data pipelines and dashboards.

Qualifications

• Master's or PhD in a quantitative field (CS, stats, math).
• 6+ years as a Data Scientist with ML and statistical modeling.
• Proficient in Python or R and ML libraries (scikit-learn, TF, PyTorch).
• Experience with Spark/Hadoop and SQL databases.
• Ability to communicate complex concepts to non-technical audiences.
• Mining or related industry experience is advantageous.

Responsibilities

• Develop ML models for predictive maintenance and exploration.
• Analyze large mining datasets to identify trends and insights.
• Design experiments and A/B tests for new algorithms.
• Collaborate with geologists, engineers and ops managers to define projects.
• Build data pipelines and dashboards to visualize KPIs and metrics.
• Stay updated on data science advances and apply to mining challenges.

Skills

Python

R

scikit-learn

TensorFlow

PyTorch

Spark

Hadoop

SQL

Communication

Education

Master's or PhD in CS/Statistics/Math

Tools

Spark

Hadoop

SQL Databases

Job description

About the Role

Our client is seeking a highly skilled and experienced Senior Data Scientist to join their innovative analytics team, working remotely from anywhere in South Africa. This role will focus on leveraging advanced data science techniques to extract insights from vast datasets related to mining operations, predictive maintenance, and resource optimization. You will collaborate with geologists, engineers, and operations managers to drive data-informed decision-making and develop cutting-edge analytical solutions. This is a unique opportunity to work on challenging problems in the mining sector with a company that embraces remote work and invests in its employees' professional growth.

Key Responsibilities

• Develop and implement machine learning models for predictive maintenance, operational efficiency, and resource exploration.
• Analyze large-scale mining datasets to identify trends, patterns, and actionable insights.
• Design and conduct A/B tests and other experiments to evaluate the effectiveness of new algorithms and strategies.
• Collaborate with stakeholders to understand business needs and translate them into data science projects.
• Build data pipelines and reporting dashboards to visualize key performance indicators and operational metrics.
• Stay abreast of the latest advancements in data science, machine learning, and AI, and apply them to mining challenges.

Requirements

• Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field.
• Minimum of 6 years of professional experience as a Data Scientist, with a focus on machine learning and statistical modeling.
• Strong proficiency in programming languages such as Python or R, and expertise in libraries like scikit-learn, TensorFlow, or PyTorch.
• Experience with big data technologies (e.g., Spark, Hadoop) and SQL databases.
• Proven ability to communicate complex technical concepts to non-technical audiences.
• Experience in the mining industry or related fields (e.g., O&G, industrial manufacturing) is highly advantageous.

Benefits

• Highly competitive salary and comprehensive remote work benefits.
• Flexible working hours and the ability to work from anywhere in South Africa.
• Generous professional development budget and access to online learning platforms.
• Contribution to a state-of-the-art remote working setup.
• Opportunity to work on impactful projects and shape the future of mining analytics.