Senior Data Scientist (Mining) at Glencore

Key Relationships

• Global Lead Data Scientist
• Data Scientist, Business Analysts, Technology Project Managers
• Regional Operational Excellence and Technology Transformation Manager
• Lead Data Engineer
• Regional Leads of Automation
• Operational Excellence Managers
• PMO Lead
• Planning and Cost Engineer
• Project Managers
• Project Services Team

Qualifications & Skill Requirements:

• Degree in Engineering, Computer Science, or a related business discipline
• Desirable Master's or PhD in Statistics, Mathematics, Computer Science, or another quantitative field.
• Agile Certification desirable
• Lean Six Sigma Certification desirable
• Project Management Certification desirable

Competencies:

• Ensures Accountability
• Develops Talent
• Drives Engagement
• Instils Trust
• Collaborates
• Customer Focus
• Plans and Aligns
• Strategic mindset
• Communicates effectively.
• Decision Quality
• Courage
• Being Resilient

Work Experience:

•   Qualifications and Experience (min 3 of the 4 points):
• More than 10 years of work experience
• More than 5 years of experience manipulating data sets and building statistical models.
• More than 1 year of experience on site mines, in operations or dispatch.
• Leadership experience, technically guiding and motivating data scientists and analysts.
• Data Science and Machine Learning Skills:
• Solid understanding of data science and machine learning foundations.
• Knowledge of a variety of machine learning techniques and their advantages/drawbacks.
• Knowledge of advanced statistical techniques and concepts.
• Experience in implementing statistical modeling and machine learning methods.
• Experience applying optimization algorithms offline and online.
• Familiarity with deep learning, NLP, reinforcement learning, combinatorial optimization, etc.

Domain Knowledge and Technical Background: 

• Experience in the mining technical field specifically on mine operations, mine control process, modular dispatch, fleet management systems, mine planning/design, and asset management.
• Experience on operation and optimization of mining vendors' applications for operations, modular dispatch, fleet management, mine planning, work management, and condition monitoring.
• Experience using and processing mining vendors' data sources for operations, dispatch, fleet management, mine planning, work management, and condition monitoring.
• Business, communication, and collaboration skills:
• Proficiency in defining and communicating business problems and opportunities against the objective of introducing solutions and improvements.
• Demonstrated, well-developed judgment and problem-solving skills with an emphasis on product development.
• Ability to promote vision and play a key role in achieving client satisfaction. 
• Ability to work well as part of a team, a diplomat, and resilience. 
• Strong verbal and written communication skills that work effectively with technical and non-technical audiences. 
• Fluent English (verbal and written) is a must, with desirable French. 

Software and Tools Proficiency:

• Familiarity with statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc. 
• Experience querying databases and using statistical computer languages: Python, SQL, R, etc. 
• Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc. 
• Experience visualizing/presenting data for stakeholders using: Periscope, Business Objects, D3, ggplot, etc. 
• Additional Attributes: Multi-tasking and excellent management of personal time and priorities. A drive to learn and master new technologies and techniques

General Accountability:

• Proactively identify and champion machine learning initiatives that are highly applicable and can innovate practices within the mining industry in the mining and resource development domains.
• Engage with Asset leadership to precisely understand specific business challenges, ensuring that these problems can be addressed using state-of-the-art analytics and AI solutions.
• Leverage the latest methodologies in deep learning, reinforcement learning, and AI to craft effective solutions for prevailing business issues.
• Play an instrumental role in mentoring and guiding other data scientists, fostering their professional development by enhancing their knowledge and skill sets.
• Work in with data engineers, machine learning engineers, and designers to architect and implement holistic analytics solutions for internal clients, ensuring these solutions bring about tangible, positive real-world impacts.
• Actively participate and contribute in a diverse, multi-disciplinary team environment, seamlessly collaborating with professionals from data science, data engineering, business, and design domains.
• Oversee and govern data models provided by external consultants to ensure consistency, reliability, and alignment with company standards.
• Maintain and monitor machine learning operations, ensuring efficient execution, up-to-date model performance, and prompt troubleshooting of any issues.
• Establish and enforce stringent test protocols and conduct advanced studies for validating or rejecting hypotheses during the rollout of technological solutions related to decision-making processes.
• Data Analysis: Analyze large datasets to extract meaningful insights and trends using, maintaining and improve the internal technology stack.
• Machine Learning Model Development: Design, develop, and deploy machine learning models tailored to specific business challenges.
• Optimization: Refine algorithms and models for improved accuracy and efficiency.
• Collaboration: Work closely with business units, data engineers, and other stakeholders to define and refine project requirements.
• Reporting: Present findings and insights to both technical and non-technical audiences in a clear and concise manner.
• Continuous Learning: Stay updated with the latest developments in the field of data science, machine learning, and AI.
• Data Governance: Ensure data quality and integrity. Establish and enforce data handling and privacy protocols.
• Mentoring: Guide and upskill junior data scientists and analysts in best practices, techniques, and methodologies.
• Tool and Platform Selection: Recommend and assist in the selection of appropriate tools and platforms for specific data tasks.
• Solution Scaling: Scale models to handle larger datasets, ensuring they remain efficient in real-world scenarios.
• Prototyping: Rapidly develop prototypes to test and validate new methodologies or concepts.
• Model Maintenance: Monitor the performance of live models, ensuring they continue to operate as expected, and adjust as needed.
• Stakeholder Communication: Engage regularly with stakeholders to ensure data initiatives align with business goals and objectives.
• Risk Management: Identify potential risks in data-driven solutions and implement strategies to mitigate them.
• Testing and Validation: Rigorously test new models and methods, ensuring their reliability before full-scale implementation.
• Documentation: Document methodologies, insights, and findings comprehensively for future reference and for other team members using Git and internal Glencore policies.
• Operational Integration: Work with IT and operations teams to integrate machine learning solutions into business operations.
• Strategic Planning: Assist in the strategic planning of data initiatives, ensuring they align with the company's long-term goals.