Senior Data Scientist

at  MINOPEX

DESCRIPTION

The Senior Data Scientist will lead a dynamic team comprising data scientists, process engineers, and operational success engineers. The role will focus on driving the scoping, development, deployment, and adoption of various data solutions, machine learning (ML) models, and data-driven systems across mineral processing operational sites. This role will be instrumental in ensuring that data solutions and ML models are fit-for-purpose, scalable, and fully aligned with the operational challenges and goals of the business This is a strategic role that will play a critical part in improving operational efficiency through advanced data solutions and technologies. The successful candidate will collaborate closely with site operations, software development teams, and other senior leadership to ensure seamless integration and effective utilization of data-driven systems.

EXPERIENCE AND SKILLS REQUIREMENTS

• 5+ years of experience in data science, with a minimum of 2 years in a senior or leadership role.
• Proven track record in developing and deploying machine learning models and data solutions in an industrial or production environment, preferably in mining or mineral processing.
• Strong expertise in programming languages such as Python or R, as well as proficiency with SQL.
• Experience in managing multidisciplinary teams, including data scientists, engineers, and analysts.
• Deep understanding of the full lifecycle of machine learning models, from data collection and feature engineering to model deployment and monitoring in production environments.
• Strong knowledge of industrial processes and their intersection with data science in driving operational improvements.
• Familiarity with databases, data pipelines, and ETL processes, as well as experience with data visualization and business intelligence tools (e.g., PowerBI, Tableau).
• Experience in project management, including scoping, resource allocation, and timelines for successful execution of data science projects.
• Strong interpersonal and communication skills, with the ability to translate complex technical concepts to non-technical stakeholders.
• Proficiency in report writing and client presentations using MS Office Word, Excel, PowerPoint, MS Projects.
• Medically fit and be able pass medical examinations at mining sites.
• Fluent in English (Read, write, and speak)

QUALIFICATION REQUIREMENTS

• Bachelor’s Degree in Data Science, Engineering, Computer Science, or a related field.
• Postgraduate qualifications (MSc or PhD) in Data Science, Machine Learning, or a similar field will be advantageous.
• Experience in mineral processing and metallurgy will be advantageous.
• Professional certifications in data science, machine learning, or related technologies are desirable.

• Lead and mentor a multidisciplinary team of data scientists, process engineers, and operational success engineers.
• Oversee the scoping, development, and deployment of machine learning models, data solutions, and analytical tools across various mineral processing sites.
• Collaborate closely with site operations teams to identify key business challenges and opportunities for data-driven solutions.
• Ensure the successful integration of ML models and systems into operational workflows, driving adoption and demonstrating value through measurable improvements.
• Provide technical oversight on the design, implementation, and maintenance of data pipelines and scalable data systems to support the organization’s digital transformation goals.
• Ensure continuous collaboration with other technical leads, including software development and systems integration, to ensure the smooth implementation of data solutions.
• Act as the main point of contact for internal and external stakeholders on all matters related to data science and machine learning.
• Stay up-to-date with advancements in data science, machine learning, and industrial applications, continuously recommending new tools, technologies, and methodologies.
• Provide strategic input to the broader technology and business strategy, ensuring that data science initiatives are aligned with business objectives.
• Monitor and evaluate the performance of data models and solutions, refining them as needed to ensure their effectiveness and accuracy over time.
• Drive a culture of innovation and continuous improvement within the data science team and the wider organization.