Lead Specialist, Data Science & Analytics II
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Key skills for this role
About the Role
Maaden is seeking a Lead Specialist in Data Science & Analytics to drive ML/AI solutions across mining and enterprise functions. The role involves end-to-end delivery of predictive models, MLOps, and stakeholder engagement.
Key Skills for This Role
Responsibilities
- Lead end to end data science delivery including problem framing, data exploration, feature engineering, model development, validation, and MLOps handover
- Develop and maintain databases and data collection systems
- Perform statistical analysis and data mining to identify patterns and trends
- Build predictive models and machine learning algorithms
- Create data visualizations and reports to communicate findings to stakeholders
- Design and maintain reliable data pipelines in partnership with data engineering
- Drive experimentation, model versioning, automated retraining, and continuous improvement
- Translate business needs into AI/analytics solutions and establish frameworks for scalability
- Ensure compliance with Responsible AI, data quality, and governance frameworks
- Communicate insights and recommendations to decision makers and track value realization
Requirements
- Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or related field
- 6+ years experience in Data Science / Advanced Analytics
- At least 2 years leading or mentoring analytics professionals
- Proficiency with ML frameworks (TensorFlow, PyTorch) and cloud platforms (Azure, AWS)
- Experience with Generative AI solutions and AI agents
- Experience with MLOps and model lifecycle management
Full Job Posting
Job Purpose
- The Lead Specialist, Data Science & Analytics acts as a technical leader driving ML/AI solutions across Maaden.
- The role ensures analytics products are designed, validated, industrialized, governed, and adopted at scale.
- The specialist analyzes data, extracts insights, and builds predictive models to improve operations and decisions.
Key Accountabilities
- Lead end to end data science delivery including problem framing, data exploration, feature engineering, model development, validation, and MLOps handover.
- Develop, implement, and maintain databases and data collection systems.
- Perform statistical analysis and data mining to identify patterns and trends.
- Build predictive models and machine learning algorithms.
- Create data visualizations and reports to communicate findings.
- Design and maintain reliable data pipelines in partnership with data engineering.
- Drive experimentation, model versioning, automated retraining, and continuous improvement.
- Translate business needs into AI/analytics solutions and establish frameworks.
- Industrialize AI/ML models with MLOps and architecture.
- Ensure compliance with Responsible AI, quality, and governance frameworks.
- Communicate insights and track value realization.
Minimum Qualifications
- Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or related field.
- 6+ years experience in Data Science / Advanced Analytics with industrial, mining, or heavy asset environments preferred.
- At least 2 years leading or mentoring analytics professionals.
- Proven ability to translate business problems into analytic approaches.
- Strong proficiency with ML frameworks (TensorFlow, PyTorch) and cloud platforms (Azure, AWS).
- Hands on experience with machine learning models including time series forecasting and predictive modeling.
- Practical experience with Generative AI solutions and AI agents.
- Experience with data engineering across IT and OT environments.
- Experience with MLOps and model lifecycle management.
- Experience with enterprise cloud platforms (Azure, Databricks, Microsoft AI Foundry).
Core Competencies
- Model Accuracy & Reliability: Performance, drift stability, and operational uptime.
- Adoption & Business Impact: Value realized, user adoption, integration success.
- Delivery Velocity: Timeliness of development cycles and deployment readiness.
- Compliance & Quality: Alignment with Responsible AI, governance, and documentation standards.
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