Senior Data Scientist

The Senior Data Scientist turns complex and often loosely defined business questions into clear, actionable insight. The role leads high-value analytical work using Python, SQL, statistical methods and appropriate machine-learning techniques to explain what is happening, understand why it is happening and assess what may happen next. The jobholder combines analytical rigour with commercial judgement, stakeholder engagement and strong written communication, ensuring that analysis results in practical business decisions.

Curious, commercially minded and highly analytical. The successful person will be equally comfortable working in Python, SQL and statistical models, sitting with a senior stakeholder to clarify an uncertain question, and stepping back to explain the  so what?  in plain language. Most importantly, they will create clarity from ambiguity, work at pace and turn analysis into decisions.

• Significant experience as a Data Analyst, Senior Data Analyst, Data Scientist, Insight Analyst or equivalent analytical professional.
• Strong analytical and problem-solving skills, including the ability to create clarity from ambiguous business questions.
• Advanced Python skills for data analysis and modelling, together with strong SQL capability.
• Practical experience applying statistical and data-science techniques to business problems, including several of forecasting, regression, classification, segmentation and propensity modelling.
• Sound understanding of statistical concepts, model evaluation, uncertainty, bias and the limitations of analytical approaches.
• Experience working with large or complex datasets and performing data discovery, validation and exploratory analysis.
• Excellent requirements-gathering, stakeholder-management and project-planning skills.
• Excellent written communication, with experience producing analytical papers or recommendations for senior audiences.
• Ability to translate complex analysis into concise conclusions, commercial implications and practical actions.
• Ability to iterate quickly, prioritise effectively and provide useful initial answers without waiting for perfectly defined requirements.
• Experience with time-series techniques such as ARIMA, exponential smoothing or related forecasting methods.
• Experience developing customer churn, lifetime value, propensity, anomaly-detection or segmentation models.
• Experience analysing financial, operational, customer or risk data.
• Experience using machine-learning techniques beyond traditional statistical analysis.
• Experience with Snowflake or another cloud data platform, Streamlit, and Power BI
• Experience in financial services or another regulated, data-rich environment.

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• 26days holiday, increasing with service, plus buy/sell scheme and bank holidays
• 7% pension with matched contributions
• Discretionary bonus scheme
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