Vice President Analytics Solutions Manager

You are a strategic thinker passionate about driving solutions in Data Analytics & AI/ML. You have found the right team.

As a Data Analytics Solutions VP within our Strategic Transformation / Data & Analytics team you will lead data-driven decision-making at scale and deliver complex analytics and data remediation outcomes across products and business lines.

• Lead descriptive diagnostic and predictive analytics to drive measurable operational improvements.
• Analyze complex multi-source datasets to identify trends drivers anomalies and actionable insights.
• Define KPIs/OKRs establish baselines and quantify benefits and outcomes of initiatives.
• Deliver executive-ready insights through clear storytelling dashboards and visualizations that drive action.
• Translate business needs into analytical solutions (hypotheses data requirements and user stories) in an agile model.
• Own end-to-end analytics delivery across data mining scalable datasets visualization and applied ML.
• Ensure data quality through validation reconciliation anomaly detection and ongoing monitoring.
• Partner with program product and technology teams to prioritize use cases define MVPs and scale data products.
• Apply AI/ML to predict outcomes embed insights into workflows and monitor model performance over time.
• Resolve complex technical challenges and unblock delivery to maintain execution momentum.
• Communicate concise status risks and issue updates to stakeholders and senior management to maintain alignment.

• Bachelors degree with relevant techno-functional experience.
• Atleast 9 years of data analytics experience supporting large-scale transformations with measurable business impact.
• Strong Capital Markets/Investment Banking knowledge with ability to translate problems into KPIs and decision frameworks.
• Proficiency with analytics/BI tooling (e.g. Alteryx Oracle/SQL Tableau QlikView/Qlik Sense).
• Ability to build repeatable data pipelines scalable datasets dashboards and insight packs.
• Strong data storytelling and presentation skills for non-technical stakeholders.
• Experience monitoring delivery via KPIs identifying risks via variance/trend analysis and executing mitigations.
• Ability to run post-implementation impact reviews and capture lessons learned.
• Strong prioritization and execution skills across multiple concurrent analytics workstreams.

• Experience working in an Agile development lifecycle.
• Proficiency in SQL and Python; familiarity with data science and ML tooling.
• Data Science certification preferred.