Test Triangle
SOLUTION ARCHITECT L2
London, GBVor OrtVertragVollzeit
Veröffentlicht am 9. Okt. 2026
Diese Stelle wird in EN ausgeschrieben
SOLUTION ARCHITECT L2: Job Description
London
- 12+ years in data science, applied ML, or advanced analytics in enterprise environments.
- Demonstrated experience extracting meaningful signals from high-volume, high-noise telemetry or behavioural datasets.
- Proficiency in Python (pandas, scikit-learn, PySpark) and statistical modelling.
- Experience building structured, repeatable analytical frameworks — use case scoring, feasibility assessments, or prioritisation models — not purely ad hoc analysis.
- Background in designing feedback loops or iterative learning models that improve future analytical outcomes .
- Experience assessing data quality and coverage sufficiency before committing analytical investment to a use case identifying key entity relationships, behavioural patterns, and signal clusters to be encoded in the graph Comfortable operating in ambiguous, early-stage data environments where frameworks need to be built from scratch.
- Experience collaborating with process or operational teams — translating data signals into process-level insights.
- Familiarity with process mining, task mining, or workforce analytics advantageous.
- Hands on experience in Python libraries of pm4py e.g. Petri Net and Display.
- Hands on databricks experience, including notebooks, creating pipelines and visualisations.
- Knowledge of NLP and applying it to data quality Structured, documented output discipline — scoring models, ranked inventories, and feasibility assessments suitable for senior review Strong remote communication and collaboration skills with onsite UK leads and Client.
- Lead Technical solutioning of Data Science Use Cases.
Nice to have: LLM, OCR, RAG based architecture
Mandatory Skills: Data Science .
Rollenübersicht
Jobart
Vollzeit
Erforderliche Kompetenzen
Data ScienceApplied Machine LearningPython (pandas, scikit-learn, PySpark)Statistical modellingSignal extraction from high-volume noisy telemetry/behavioural datasetsBuilding structured analytical frameworks (use case scoring, feasibility, prioritisation)Designing feedback loops and iterative learning modelsData quality assessment and coverage sufficiencyGraph modelling and encoding of entity relationships and signal clustersDatabricks (notebooks, pipelines, visualisations)pm4py (Petri Net, Display) / process mining toolsNatural Language Processing applied to data qualityTranslating data signals into process-level insights (collaboration with operations)Technical solutioning / Solution Architecture for Data Science use cases
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