Zorba Consulting - Business Analyst

Description:

About the Role

We are looking for a highly analytical, detail-oriented, and business-focused AI Business Analyst to act as the bridge between business stakeholders and technical AI/ML teams. The ideal candidate will be responsible for understanding business problems, translating them into data/AI use cases, documenting clear requirements, validating model outputs, and ensuring successful delivery of AI-driven solutions.

This role requires a strong blend of analytical thinking, business process understanding, and familiarity with the AI/ML development lifecycle.

Key Responsibilities

1. Requirement Gathering & Business Analysis

- Interact with business stakeholders to understand needs, challenges, and opportunities for AI-driven improvement.

- Translate business requirements into detailed functional requirements, user stories, and acceptance criteria.

- Conduct feasibility assessments for new AI/Data/Analytics initiatives.

- Maintain requirement documents, process maps, user journeys, and feature documentation on Confluence/Jira.

2. AI/ML Use Case Definition

- Identify potential AI/ML use cases and create use-case charters with business impact, expected outcomes, and KPIs.

- Work closely with data scientists to refine objectives, define data needs, and determine suitable model evaluation metrics.

- Support prioritization of AI projects based on ROI, effort, and business alignment.

3. Data Analysis & Validation

- Use SQL and data modeling concepts to explore, validate, and analyze data sets.

- Perform data profiling to support data quality assessments and identify data gaps.

- Assist in feature definition for models with basic data understanding.

4. Model Testing, Evaluation & Acceptance

- Collaborate with AI/ML engineers to understand model behavior, inputs, and outputs.

- Validate model performance using KPIs, evaluation metrics, and domain-specific logic.

- Prepare and execute UAT test cases, report defects, and provide sign-off for production deployment.

- Continuously monitor deployed model outputs for consistency and drift.

5. Reporting & Insights

- Build dashboards and reports using Power BI, Tableau, or equivalent BI tools to communicate insights.

- Prepare business cases, post-implementation impact analysis, and performance summaries.

- Present findings and recommendations to senior stakeholders in a clear and structured manner.

6. Cross-functional Collaboration

- Work closely with Data Scientists, Data Engineers, Product Managers, and IT teams to ensure smooth execution of AI projects.

- Act as the primary conduit between technical teams and business users.

- Ensure alignment across teams and resolve ambiguities in requirements or expected outcomes.

7. Project Management Support

- Track project milestones, risks, dependencies, and progress in Jira.

- Assist in sprint planning, backlog grooming, and status reporting.

- Ensure documentation and deliverables are completed on time and meet quality standards.

Technical Skills Required

Data & Databases

- SQL (strong ability to query, validate, and analyze datasets)

- Understanding of relational & NoSQL databases

- Data modeling fundamentals

AI/ML Knowledge

- Understanding of machine learning lifecycle, training workflows, and evaluation metrics

- Familiarity with concepts like data preprocessing, classification, regression, drift, model accuracy, precision/recall, etc.

- Exposure to AI platforms like Azure AI is preferred

- Python basics (optional, but a plus)

Tools & Platforms

- Jira, Confluence

- Excel (advanced) pivot tables, macros, analysis

- BI Tools Power BI, Tableau, etc.

Testing & Documentation

- Writing test cases, UAT scripts, acceptance criteria

- Validating data, model outputs, and dashboards

- Strong documentation and reporting capabilities

Required Qualifications

- Masters degree in Computer Science, Data Science, Business Analytics, Information Systems, or related field.

- 3 - 6 years of experience as a Business Analyst, preferably in AI, ML, Data, or Analytics environments.

- Strong communication, stakeholder management, and problem-solving skills.

- Ability to translate business language into data/AI requirements with clarity and precision.

Preferred Attributes

- Experience in AI/ML implementation projects (NLP, recommendation systems, predictive analytics, etc.)

- Ability to work with cross-functional and global teams

- Strong attention to detail with structured thinking

- Comfortable working in agile environments