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Key Responsibilities:
- Enterprise Data Integration – Consolidating data across the enterprise into a single source of truth through modern data warehousing and lakehouse architectures.
- Business Intelligence Enablement – Developing and maintaining enterprise-level analytics to support the monitoring and optimization of key business functions such as Finance, Supply Chain, Customer Service, Project Management, and Engineering.
- Applied Machine Learning for Equipment Monitoring – Designing and deploying machine learning models within web applications to enable predictive maintenance, anomaly detection, failure prediction, and estimation of the remaining useful life (RUL) of spare parts.
- Stakeholder Engagement and Ad Hoc Analytics – Collaborating with internal and external stakeholders to address data-driven inquiries and support project-specific analysis related to equipment performance and operational efficiency.
- Insight Communication and Data Storytelling – Delivering presentations and effectively communicating analytical insights to a wide range of stakeholders, ensuring that data-driven strategies are clearly understood and actionable.
To apply immediately for this position click here: www.totalrecruitment.solutions/candidate_registration_1.aspx?JobID=78043&referrer=Unique
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Technical Skills
- Data Engineering & Integration
- Proficiency with Microsoft Fabric (OneLake, Lakehouse, Data Warehouse) - advantageous
- Experience with ETL/ELT pipeline development (e.g., Azure Data Factory, Synapse Pipelines)
- Strong knowledge of Kimball dimensional modeling (star vs. snowflake schema)
- SQL (T-SQL, M code)
- Data ingestion from various sources (ERP, On-prem and Cloud databases, CRM, SharePoint, OneDrive, etc.)
- Knowledge of Data Gateways for on-prem data and cloud resources integration.
- Knowledge of Databases (PostgreSQL, SQL Server)
Technical Skills
Data Engineering & Integration
- Proficiency with Microsoft Fabric (OneLake, Lakehouse, Data Warehouse) - advantageous
- Experience with ETL/ELT pipeline development (e.g., Azure Data Factory, Synapse Pipelines)Strong knowledge of Kimball dimensional modeling (star vs. snowflake schema)
- SQL (T-SQL, M code)
- Data ingestion from various sources (ERP, On-prem and Cloud databases, CRM, SharePoint, OneDrive, etc.)
- Knowledge of Data Gateways for on-prem data and cloud resources integration.
- Knowledge of Databases (PostgreSQL, SQL Server)
Machine Learning & Advanced Analytics
- Python (primary), R (optional), Spark (beneficial)
- Time series forecasting (e.g., ARIMA, Prophet, LSTM, etc.) -
- Descriptive and Inferential Statistics
- Predictive Maintenance Modelling: failure prediction, anomaly detection, RUL estimation
- ML frameworks such as Scikit-learn
- Model Deployment using Azure Machine Learning, Azure Functions, or AKS
Business Intelligence
- Strong skills in Power BI (Data Modelling, M Code, DAX, dashboards)
- Experience answering ad hoc queries and interpreting complex datasets
- Ability to perform root cause analysis and correlation studies on equipment performance
Software & Cloud Development – Advantageous
- Familiarity with REST APIs and microservices for integrating ML models into web applications.
- Understanding of web app deployment and hosting on Azure App Services
Supply Chain & Inventory Analytics – Advantageous
- Understanding of inventory control, demand planning, working capital optimization
- Experience with inventory optimization models (e.g., EOQ, reorder point models, ABC analysis)
Qualification:
- Degree in Computer Science, Engineering or related field
Experience:
- 1-3 years + experience within a data science environment, preferably with mining and mineral processing experience or supply chain experience
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