Data Analyst: Root Cause & Product Improvement

Our financial services client is looking for an analyst with an industrial-engineering style improvement mindset, someone who uses data to find the real problem, builds the case for fixing it, and then works with product teams until it is fixed.

We are not looking for a data engineer, data architect or BI developer. Profiles that lead with pipeline build, warehouse architecture, cloud platforms or dashboard development are not a fit, regardless of seniority.

• Ad hoc data analysis and deep dives across the client product lines.
• Root-cause analysis: taking a symptom such as a complaint spike, failure rate or drop-off and tracing it to the underlying cause.
• Building business cases that quantify the issue and size the opportunity.
• Partnering with product teams to prioritise and drive remediation through to closure.
• Presenting findings and recommendations to senior and executive stakeholders with a clear call to action.

Note:

this role does not involve process mapping, and it does not involve building or maintaining data pipelines.

• Confident working with and interpreting data independently: can get to a dataset, interrogate it and draw defensible conclusions.
• Able to query multiple different but interrelated data sources and join them into a single coherent view. Most root causes only surface when data from several systems is pulled together.
• Demonstrated root-cause analysis on real business problems.
• SQL important, but a working level is sufficient. They must be able to query data themselves without depending on an engineer. Query optimisation and performance tuning are not required.
• Strong stakeholder management: influencing, negotiating and holding other teams to remediation commitments.
• Clear communication and data storytelling to non-technical and executive audiences.
• Proactive and curious: challenges vague requests and works out what the business actually needs.

• Python or R, advanced Excel or VBA, statistical or predictive modelling.
• Power BI or similar visualisation tooling.
• Banking or financial services exposure.
• Exposure to complaints, incident, operational or customer journey data