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.
Job Responsibilities
- 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.
Required qualifications, capabilities and skills
- Bachelor’s 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.
Preferred qualifications, capabilities and skills
- Experience working in an Agile development lifecycle.
- Proficiency in SQL and Python; familiarity with data science and ML tooling.
- Data Science certification preferred.