Marcus by Goldman Sachs, MI Manager, Savings Analytics & Forecasting, Associate, Birmingham

Your Impact

• Bring a customer-focused, decision-ready view of performance by delivering clear MI and forecasting that helps teams understand how customers are behaving (inflows, withdrawals, retention, maturities) and what actions to take next, aligned to the outcomes focus of Consumer Duty.
• Strengthen the bank's ability to run smoothly day-to-day by providing timely, accurate, and well-explained reporting across Commercial, Products, Marketing, Customer & Telephony Operations, and Risk, enabling leaders to spot issues early, prioritise fixes, and track improvements.
• Improve process innovation and efficiency by reducing manual reporting effort through better BI-ready datasets, automation, and repeatable reporting routines, supported by robust data management practices.
• Reinforce risk management and control by ensuring MI and forecasting are well governed: consistent KPI definitions, reconciliation and data quality checks, controlled distribution, and audit-ready documentation; operating appropriately under UK GDPR.
• Support better pricing and planning decisions by providing scenario-based insights on how rate changes and market movements may affect savings balances and flows, and by applying proportionate model governance practices consistent with banking expectations.

Summary & Responsibilities Forecasting & Decision Support (Flows, Balances, Pricing)

• Savings Forecasting Support: Deliver forward views of savings balances and flows (gross inflows, withdrawals, net flows, maturity roll-offs), segmented by product and cohort, using appropriate forecasting techniques.
• Scenario & Sensitivity Analysis: Provide scenarios to support planning and pricing (e.g., rate moves, competitor positioning, seasonal impacts), with clearly stated assumptions and limitations.
• Model Governance (proportionate): Apply proportionate documentation, monitoring, and controls for forecasting models in line with banking model risk management expectations.

Collaboration & Stakeholder Alignment

• Cross-functional Collaboration: Partner with Engineering, Data Platform, Product & Pricing, Treasury/ALM, Finance, Marketing, Operations, and Risk to align on data definitions, metrics, and delivery priorities.
• Data Source Development: Identify gaps in reporting and forecasting coverage; collaborate with stakeholders to onboard or develop new data sources needed for comprehensive MI and analytics.
• Knowledge Sharing: Maintain clear documentation and run book style materials to support transparency, continuity, and efficient onboarding for analysts and stakeholders.

Reporting Delivery & Performance Insight

• Reporting Delivery: Develop, maintain, and enhance MI packs, dashboards, and recurring performance views for various forums ensuring clear commentary and "so what" insights.
• Release Management: Manage the release lifecycle for dashboards and reports (testing, stakeholder sign off, timely deployment), with clear change notes and version control.
• Method Consistency: Ensure consistent application of definitions, reporting methodologies, and segmentation logic across divisions and channels.

Data Stewardship & Governance

• Data Stewardship: Act as a data steward for savings MI domains, ensuring data is accurate, consistent, reconciled, and fit for purpose across source systems and reporting tools.
• Data Governance & Controls: Implement and uphold governance practices covering KPI definitions, data quality standards, access controls, and controlled MI distribution, aligned to UK GDPR expectations.
• Auditability: Maintain an audit ready trail of metric definitions, data lineage, key controls, and changes to critical reporting outputs.

Data Management, Pipelines & BI-ready Datasets

• Data Management (ETL/ELT): Support end-to-end management of reporting datasets, including extraction, transformation, and loading into BI tools, ensuring integrity, availability, and refresh reliability.
• BI-ready Data Products: Shape and refine curated datasets (clean, well-structured, documented) that can be reused across MI, forecasting, and pricing analytics.
• Monitoring & Troubleshooting: Monitor pipelines and reporting solutions for performance issues, investigate discrepancies, and coordinate fixes to minimise outages and data quality incidents.

Requirements

• Relevant experience: Proven experience in MI reporting, analytics, and data management (or similar), preferably in banking/financial services, ideally supporting retail savings/deposits.
• SQL & data investigation: Strong proficiency in SQL, including building trusted datasets, performing reconciliations, and investigating anomalies/root causes.
• BI & visualisation: Strong proficiency in data visualisation and dashboarding (e.g., Tableau), with the ability to deliver clear, stakeholder-ready MI and insights.
• Data governance & quality: Sound understanding of data governance principles (definitions, lineage, quality controls, ownership), and experience applying them in practice.
• Data pipelines & reporting datasets: Experience working with data engineering and/or platforms supporting ETL/ELT processes and BI-ready datasets, with an emphasis on integrity and availability.
• Risk, privacy & controls mindset: Comfortable operating in a controlled environment, including appropriate handling of personal data and controlled MI distribution aligned to UK GDPR expectations.
• Customer outcomes focus: Able to connect MI and insight to improved customer outcomes and fair value decisioning, consistent with Consumer Duty expectations.
• Analytical capability: Excellent analytical and problem-solving skills, strong attention to detail, and ability to translate data into clear "so what / what next" actions.
• Stakeholder management: Exceptional communication skills, able to explain complex data concepts to both technical and non-technical audiences, and influence priorities across teams.
• Leadership behaviours: Desire to help others meet targets and develop skills; ability to drive continuous improvement and ways of working.
• Ways of working: Self directed, detail-oriented, adaptable, and effective in a fast paced, team oriented environment; high standards for service and delivery.

Preferred Skills & Qualifications

• Education: Bachelor's degree in Data Science, Computer Science, Statistics, or a related quantitative field.
• Regulatory awareness (banking): Working knowledge of key regulatory expectations affecting MI, data handling, and customer outcomes, such as:
  • UK GDPR / Data Protection Act principles (lawful processing, minimisation, access control)
  • PSD2 concepts (payments ecosystem, third party access context)
  • Consumer Duty outcomes based expectations
• BI design capability: Strong understanding of BI concepts and best practice (KPI design, semantic/metric consistency, dashboard design for decision making).
• Data warehousing & modelling: Familiarity with data warehousing concepts and data modelling techniques (e.g., star schemas, curated layers, governed dimensions/measures).
• Cloud data platforms: Experience with modern cloud data platforms, ideally:
  • Snowflake for data warehousing and analytics workloads
  • AWS services used in data storage/processing and pipeline operations
• Big data exposure (advantageous): Awareness of big data technologies and distributed processing patterns used in cloud analytics environments.
• Customer case management tooling (nice to have): Familiarity with customer case management/CRM tooling to incorporate operational customer insights into MI and root cause analysis (e.g., contact drivers, complaint themes, service performance).

Equal Employment Opportunity Statement

Goldman Sachs is an equal employment/affirmative action employer Female/Minority/Disability/Veteran/Sexual Orientation/Gender Identity.