SOLUTION ARCHITECT L2

• 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

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