Senior Business Analyst

Straive; Taguig; Hybrid; PHP 600,000 - 1,000,000; Full time

Job summary

Straive is seeking a Business Analyst to bridge Treasury/Corporate Finance operations with AI-driven data science initiatives. The role requires domain fluency in finance, data literacy, and the ability to communicate with data scientists.

A hybrid work setup is available to balance analytical rigor with stakeholder engagement. The ideal candidate will map data lineage, suggest meaningful features, and understand model limitations while working within Agile/Scrum processes.

Qualifications

• Strong Treasury & Corporate Finance domain knowledge.
• Ability to map data lineage and identify relevant variables.
• Familiar with ML concepts, explainable AI, and model limitations.

Responsibilities

• Bridge finance operations data needs with data science teams.
• Define acceptance criteria for probabilistic AI outputs.
• Collaborate in agile sprints and manage user stories.

Skills

SQL Proficiency

Data Visualization

Agile/Scrum

Tools

Power BI

Tableau

SQL Databases

Job description

Business Analyst (BA) for a Treasury or Finance AI project, you need a hybrid professional who bridges the gap between complex financial operations and data science. A standard IT BA will struggle with the financial nuances, while a pure financial analyst will struggle with machine learning workflows.

Required Skills:

1. Domain Expertise (Treasury & Corporate Finance)

The BA must deeply understand what they are trying to automate or predict. Look for strong knowledge in:

• Cash Flow Forecasting: Understanding liquidity management, working capital cycles, and variance analysis.
• Risk Management: Knowledge of foreign exchange (FX) hedging, interest rate risk, and counterparty risk.
• Financial Instruments: Familiarity with money market funds, commercial paper, bonds, and derivatives.Treasury Systems: Experience working with Treasury Management Systems (TMS) (like SAP Treasury, Kyriba, or Quantum) and ERP systems (Oracle, SAP).

2. AI & Data Literacy (The "AI" Bridge)

They do not need to code algorithms, but they must speak the language of Data Scientists.

• Data Mapping & Lineage: Ability to identify where financial data lives, its format, and how clean it is. (AI projects fail without clean data).
• Feature Engineering Input: The capacity to tell data scientists which business variables matter (e.g., "Quarter-end seasonality affects this cash flow line item").
• Understanding Model Limitations: A foundational grasp of how Machine Learning (ML) works, including concepts like training data, over-fitting, and confidence scores.
• Explainable AI (XAI): The ability to help translate a black-box AI prediction into a rationale that a Chief Financial Officer (CFO) or Treasurer will trust.

3. Core Business Analysis & Agile Delivery

• Requirement Gathering for Unpredictable Outputs: Unlike traditional software, AI outputs are probabilistic (predictions), not deterministic (fixed rules). The BA must be skilled at defining acceptance criteria for probabilistic data.
• SQL Proficiency: Must-have ability to query databases independently to validate data assumptions before handing them to the development team.
• Data Visualization: Experience using tools like Power BI or Tableau to mock up how the AI insights will be delivered to executives.
• Agile/Scrum Framework: Experience working in iterative sprints, managing product backlogs, and writing user stories.