Job Description

London

12+ years in data science, applied ML, or advanced analytics in enterprise environmentsDemonstrated experience extracting meaningful signals from high-volume, high-noise telemetry or behavioural datasetsProficiency in Python (pandas, scikit-learn, PySpark) and statistical modellingExperience building structured, repeatable analytical frameworks — use case scoring, feasibility assessments, or prioritisation models — not purely ad hoc analysisBackground in designing feedback loops or iterative learning models that improve future analytical outcomesExperience assessing data quality and coverage sufficiency before committing analytical investment to a use caseidentifying key entity relationships, behavioural patterns, and signal clusters to be encoded in the graphComfortable operating in ambiguous, early-stage data environments where frameworks need to be built from scratchExperience collaborating with process or operational teams — translating data signals into process-level insightsFamiliarity with process mining, task mining, or workforce analytics advantageousHands on experience in Python libraries of pm4py e.g. Petri Net and DisplayHands on databricks experience, including notebooks, creating pipelines and visualisationsKnowledge of NLP and applying it to data qualityStructured, documented output discipline — scoring models, ranked inventories, and feasibility assessments suitable for senior reviewStrong remote communication and collaboration skills with onsite UK leads and ClientLead Technical solutioning of Data Science Use CasesNice to have: LLM, OCR, RAG based architecture