Senior Data Scientist
Clearance: Active SC required
Salary: £80,000-£90,000
Position Overview
As a Senior Data Scientist, you will lead advanced analytical investigations that reveal structure, relationships, and operational insight from complex, high-volume data streams. You will architect workflows for pattern identification, anomaly detection, and interaction analysis across diverse data sources, often involving tracked entities, sensor feeds, or behavioural signals.
You will also define and implement quality assurance methodologies that ensure analytical outputs are consistent, robust, and interpretable, working closely with engineers to embed those checks into production systems. In addition, you will take ownership of high-value or time-sensitive analytical requests from internal and external stakeholders, translating open-ended questions into clear, reliable, data-driven answers.
Responsibilities
* Design and lead investigations into patterns, trends, and edge cases across filtered datasets.
* Develop interaction models and combined analyses across multiple entity types and data modalities.
* Design data validation, anomaly checks, and analytical reliability frameworks to ensure outputs behave correctly across varied data inputs.
* Partner with solutions and data engineering to embed analytic logic into data pipelines and services.
* Conduct bespoke, high-complexity analysis in support of customer-facing or operational needs.
* Guide team best practices in Spark SQL usage, data documentation, and reproducible exploratory analysis.
Desired Qualifications
* 5+ years of experience in data science, applied analytics, machine learning, or analytical R&D.
* Advanced expertise in Python and distributed compute frameworks such as Spark or Databricks, including strong proficiency in Spark SQL.
* Strong background in statistical inference, anomaly detection, clustering, interaction modelling, or other analytical methods suited to large and heterogeneous datasets.
* Experience working with multi-source, semi-structured, geospatial, or entity-centric data, with a strong ability to derive insight from complex operational environments.
* Demonstrated success building data quality, validation, or reliability frameworks, particularly for analytical workflows or model-adjacent processes.
* Ability to translate ambiguous analytical problems into structured, reproducible investigation plans.
* Excellent communication, mentorship, and cross-functional collaboration skills.
Nice to Have
* Experience with MLflow, feature stores, or MLOps platforms.
* Familiarity with model lifecycle management, reproducibility tooling, or production model monitoring.
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