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# Data Scientist

- **Company:** Trust In Soda Ltd
- **Location:** UK (Remote available)
- **Experience:** Experienced
- **Salary:** £78,000.0
- **Contract:** Temporary contract
- **Skills:** Data Governance, Python (Programming Language), Standard Sql, Management of Software Versions, Low-code, Data Pipelines
- **Published:** July 20, 2026
- **Apply:** https://www.trustinsoda.com/jobs/data-scientist-35127626#apply-job

## About the Role

* 4+ years in data science, analytics engineering, or a closely related field, with demonstrated delivery of production-grade data and ML solutions.
 * Hands-on experience with Microsoft Fabric, including OneLake, Lakehouses, Data Warehouses, and Power BI semantic models.
 * Practical experience with Fabric IQ, specifically building or maintaining ontologies that define business entities, relationships, properties, rules, and actions, and aligning them with existing Power BI semantic models. (Note: ontology is currently in preview, so candidates from late-2025/2026 hands-on programs are realistic.)
 * Experience designing, building, and publishing Fabric Data Agents, including the low-code experience and the Fabric Data Agent Python SDK; understanding that Data Agents operate under the end user's identity and respect underlying Fabric data permissions (Entra ID).
 * Strong SQL and Python; familiarity with semantic modeling concepts (measures, hierarchies, dimensions).
 * Understanding of data governance, lineage, and permissions-aware access in an enterprise context.

Preferred / nice-to-have:

 * Experience with Fabric Real-Time Intelligence (Eventhouse, live signals) and graph in Fabric for cross-domain reasoning.
 * Familiarity with Operations Agents and human-in-the-loop approval patterns (e.g., Teams-based action approval, autonomous-rule promotion).
 * Exposure to the broader Microsoft IQ context layer (Work IQ, Foundry IQ) and to exposing ontologies to external agents via MCP.
 * Experience with NL2Ontology / natural-language querying over a semantic layer.
 * Background treating the semantic layer as production infrastructure - versioning, testing, and governing ontologies with the same discipline as data pipelines.



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