Lead Specialist, Data Science & Analytics II
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Key skills for this role
About the Role
Job Purpose The Lead Specialist, Data Science & Analytics, acts as a technical leader and senior practitioner, driving development, deployment, and scaling of Machine Learning,.
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
- Translate business needs into AI/analytics solutions by engaging stakeholders and building value hypotheses
- Industrialize AI/ML models through MLOps and architecture integration with data engineering and cloud teams
- Ensure responsible AI, quality, and governance compliance
- Communicate insights and track value realization
Requirements
- Expertise in statistics, computer science, and business strategy
- Experience in building predictive models and machine learning algorithms
- Ability to lead end to end data science delivery
- Knowledge of MLOps and model governance
- Strong stakeholder management and communication skills
Full Job Posting
Job Purpose
- The Lead Specialist, Data Science & Analytics, acts as a technical leader and senior practitioner, driving development, deployment, and scaling of Machine Learning, AI, and advanced analytics solutions across Maaden.
- The role ensures analytics products are designed, validated, industrialized, governed, and adopted at scale, providing measurable value across mining, processing, operations, and enterprise functions.
Key Accountabilities
- Lead End to End Data Science Delivery: Own the full lifecycle of ML/AI initiatives from problem framing to MLOps handover.
- Translate Business Needs into AI/Analytics Solutions: Establish frameworks and operating models that make data science accessible and scalable.
- Industrialize AI/ML Models (ML Ops & Architecture): Partner with data engineering and cloud teams to embed models into enterprise systems.
- Responsible AI, Quality & Governance: Ensure compliance with Maaden's Responsible AI, data quality, and data governance frameworks.
- Stakeholder Management & Value Realization: Communicate insights and track value realization.
Skills and Expertise
- Developing, implementing and maintaining databases and data collection systems.
- Performing statistical analysis to understand and interpret data insights.
- Applying data mining techniques to identify patterns, trends, and relationships in large datasets.
- Building predictive models and machine learning algorithms to forecast future outcomes.
- Creating clear data visualizations and reports to communicate findings to stakeholders.
- Working with cross functional teams to understand business needs and provide data driven solutions.
- Design and maintain reliable data pipelines and models in partnership with data engineering.
- Ensuring data security and compliance with relevant regulations.
- Drive experimentation, model versioning, automated retraining, and continuous improvement.
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