Data Scientist (𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 & 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀)

Company Description LogiX helps organizations unlock the full value of their data by transforming business operations through robust data strategies and AI-driven solutions. The company partners with clients across industries to design and implement data platforms that drive smarter decisions and measurable outcomes. At LogiX, data and AI are treated as core enablers of innovation, efficiency, and competitive advantage. Team members work closely with stakeholders to turn complex data challenges into scalable, practical solutions that support long-term growth.Role Description • Data pipelines & engineering: Build reliable ingestion and transformation pipelines (Python, SQL, Azure Data Factory) from SAP and M365 into a governed central platform.• Data quality & observability: Design production-grade validation, reconciliation, anomaly detection and quarantine so every figure is traceable, accurate and monitored end-to-end.• Analytics & modelling: Analyse data to uncover trends, anomalies and business drivers; apply statistical analysis, forecasting and ML where they materially improve decision-making.  Mandatory Expertise (7 years experience +)Azure Data Engineering – practical experience with Azure Data Factory and Azure-based data platforms, including pipeline orchestration, monitoring and troubleshooting.SAP Data Integration – experience extracting, integrating and reconciling data from SAP systems, including understanding SAP data structures and source-to-target mapping.Microsoft 365 / Microsoft Graph Integration – experience ingesting data from M365 sources and APIs, ideally Microsoft Graph.Data Quality Engineering – hands-on experience designing automated validation, reconciliation, completeness, consistency and integrity checks.Data Observability & Exception Handling – ability to implement data freshness monitoring, anomaly detection, logging, alerting, quarantine/reprocessing and traceability.Advanced Python and SQL – strong hands-on capability building production-grade data ingestion, transformation, validation and analytical workflows.Data Lineage & Auditability – experience ensuring data can be traced from source through transformations to final reporting/analytics outputs.ETL/ELT & Data Modelling – strong understanding of scalable ETL/ELT patterns, transformation logic, schemas and analytical data models.Statistical Analysis & Anomaly Detection – ability to investigate trends, outliers, variances and business drivers using appropriate statistical techniques.Forecasting / Applied ML – practical experience applying forecasting or machine-learning techniques to structured enterprise data where appropriate.Production Engineering Mindset – experience taking data pipelines and analytical solutions beyond prototypes into reliable, monitored production environments.