Data Analytics Associate

About the Role

As a Financial Analytics Associate within the Asset and Wealth Management Transformation and Analytics Team, you will define, refine, and deliver strategic goals by translating complex financial and operational data into actionable insights, predictive models, and AI-promoted solutions.

Key Responsibilities

• Scope, prioritize, and coordinate activities with product owners and finance stakeholders.
• Partner with Finance, Risk, and Business teams to identify strategic analytical, data science, and AI solutions.
• Perform in-depth financial analysis including revenue, expense, margin, variance, profitability, product/client profitability, and KPI analysis.
• Design, develop, and automate management reporting, dashboards, and scorecards covering P&L, balance sheet, cash flow, forecasts, budgets, headcount, and performance metrics.
• Drive annual budgeting, mid-year re-forecast, rolling forecast, and multi-year strategic planning cycles.
• Implement Exploratory Data Analysis (EDA) and statistical modelling to identify trends, patterns, and drivers of financial performance.
• Identify key financial and operational data sources, ensuring data quality, reconciliation, and audit trails.
• Design and develop data mining and reporting queries from large datasets, converting raw financial data into clear information.
• Build visualizations and storytelling using BI tools (Tableau, Power BI, Alteryx) for senior leadership.
• Employ scaling and automation for data preparation, consolidation, and reporting workflows.
• Transform model outputs and analytical results into digestible formats for end users through visualizations, tables, and presentations.
• Assist technical and non-technical stakeholders in interpreting analysis.
• Leverage AI assistants and copilots (e.g., Microsoft 365 Copilot, GitHub Copilot, in-house Gen AI tooling) to accelerate analytics workflows (code generation, SQL/Python drafting, document summarization, natural-language querying) with firm-approved controls.

Requirements & Tech Stack

• Self-starter, attention to detail, results-oriented, able to work under minimal guidance.
• Uses pertinent financial data and facts to identify and solve business problems.
• Mandatory experience in financial analytics, FP&A, management reporting, variance analysis, and KPI/performance measurement.
• Strong understanding of accounting principles (US GAAP / IFRS), financial statements (P&L, balance sheet, cash flow), journal entries, intercompany, allocations, accruals, and month-end close processes.
• Working knowledge of profitability and performance metrics (unit economics, gross/operating margin, contribution margin, EBITDA, ROE/ROA, cost-to-income ratio, NIM, fee revenue, AUM-based metrics).
• Mandatory experience in Exploratory Data Analysis (EDA), regression, and statistical/machine learning modelling applied to financial or business datasets.
• Hands-on experience with pandas and PySpark/PySQL stack.
• Experience in building data extraction, transformation, and modelling pipelines.
• Strong proficiency in SQL (Snowflake, Databricks) and Python (Pandas, NumPy, Scikit-learn, Keras, PySpark/PySQL) for data wrangling, modelling, AI pipelines, and automation.
• Hands-on experience using AI skills / copilots to support analytics work (e.g., Microsoft 365 Copilot, GitHub Copilot) for SQL generation, data summarization, and productivity gains.
• Awareness of responsible-AI, data-privacy, and model-risk guardrails.
• Working proficiency in data visualization tools; strong experience with BI technologies including Tableau, Power BI, and Alteryx.
• Experience in end-to-end implementation of Business Intelligence (BI) reports and dashboards.
• Good mathematics, inferential statistics, and quantitative analysis skills applied to financial data and model building.
• Provides explanations and information to others; strong business partnering skills.
• Good communication and analytical skills, with the ability to present financial insights, model results, and AI-driven outputs clearly.

Qualifications

• Bachelor's Degree in Finance, Accounting, Economics, Statistics, Data Science, Computer Science, or Engineering. A different field of study with significant professional experience in Financial Analytics / BI / Data Science / AI is acceptable.
• MBA (Finance) is a strong plus.
• Exposure to Asset & Wealth Management / banking products and metrics (AUM, net flows, revenue yield, client profitability, fee waivers) is strongly preferred.