Data Scientist

Introduction

This position will be based at Head Office in Woodmead.

Description

The purpose of this role is to analyse, process, model and interpret the data to create actionable plans for the 13 operational sites across the EMEA region. The Data Scientist will be a valuable member of a dynamic department that operates similarly to a startup within a larger corporate structure, driving innovation and implementing creative solutions.

Qualification Requirements

1. Bachelor’s degree in industrial engineering, Chemical/Process Engineering, Computer Science, Electrical Engineering, Metallurgical Engineering or in a relevant field.
2. Post Graduate qualification in data science or related fields will be advantageous.

Experience And Skills Requirements

1. 3+ years data science experience.
2. Deep understanding on the fundamental workings behind different machine learning algorithms.
3. Strong programming skills in languages such as Python or R and SQL.
4. Experience in applied machine learning in industrial contexts.
5. Understanding of deploying a machine learning in a production environment.
6. Familiarity with database technologies and data warehousing concepts.
7. Experience in data analysis with data manipulation, cleaning, and wrangling techniques.
8. Good communicator and interpersonal skills.
9. Proficiency in report writing and client presentations using MS Office Word, Excel, PowerPoint, MS Projects.
10. Medically fit and able to pass medical examinations at mining sites.
11. Fluent in English (Read, write, and speak).
12. Experience in industrial mineral processing would be advantageous.

Duties and Responsibilities

1. Work closely with stakeholders to identify business problems and opportunities for data-driven solutions.
2. Collect, clean, and manipulate large datasets to extract insights and generate predictive models.
3. Apply advanced statistical and machine learning techniques to build and deploy models that can make accurate predictions and recommendations.
4. Communicate findings to technical and non-technical stakeholders through reports, visualizations, and presentations.
5. Collaborate with other data scientists, engineers, and business analysts to develop scalable and efficient data pipelines and workflows.
6. Working in collaboration with various sites is an essential part of the role, which involves gaining an understanding of the process context, data collection, and extraction methods.
7. Continuously evaluate and improve models and algorithms to ensure their accuracy and effectiveness.
8. Stay up to date with new data science and machine learning technologies and methodologies and recommend new tools and approaches for solving business problems.
9. Execution of analytical projects and investigations (initiation, planning, execution, monitoring and control and closure).
10. Conducting literature surveys.
11. Management of client expectations through regular interactions.
12. PowerBi development and data linking.