What are the responsibilities and job description for the Information Technology Business Analyst position at Hallmark Global Solutions Ltd?
Role Summary
Looking for a Business Analyst with AI exposure who can act as a strong bridge between business stakeholders, data science / AI teams, and engineering. The ideal candidate will bring core BA skills (requirements, process modeling, stakeholder management) along with hands-on or working knowledge of AI/ML, analytics, and automation-driven use cases.
This role supports AI‑enabled transformation initiatives, including process automation, intelligent decisioning, predictive analytics, and agentic‑assisted workflows.
Key Responsibilities
Business & Functional Analysis
- Elicit, analyze, and document business, functional, and non-functional requirements for AI‑enabled solutions.
- Work closely with business stakeholders to identify AI / analytics use cases that improve efficiency, productivity, risk control, or customer experience.
- Translate business problems into AI problem statements (e.g., prediction, classification, recommendation, NLP use cases).
- Create process flows, user journeys, use cases, and acceptance criteria for AI-driven capabilities.
- Support UAT, validation of AI outputs, and business sign‑off.
AI / Data Collaboration
- Act as a liaison between business, data science, and engineering teams.
- Support definition of:
- Training data requirements
- Data quality rules
- Model assumptions and constraints
- Assist in interpreting model outputs, confidence scores, explainability (XAI), and business impacts.
- Partner with teams delivering ML models, LLM‑based solutions and agentic AI capabilities.
Process & Automation Enablement
- Support automation initiatives by mapping as‑is / to‑be processes and measuring productivity gains.
- Track benefits such as cost optimization, cycle time reduction, productivity uplift, and quality improvements.
Governance & Documentation
- Ensure compliance with data privacy, model governance, and regulatory expectations (especially in BFSI environments).
- Maintain structured documentation:
- BRDs / FRDs
- AI use‑case canvases
- Data dictionaries
- Model impact assessments
Required Skills & Experience
Core BA Skills
- 10 years of experience as a Business Analyst / Techno‑Functional BA.
- Strong experience in:
- Requirements gathering
- Stakeholder management
- Agile delivery (user stories, backlog grooming)
AI / Analytics Exposure (Must‑Have)
- Working knowledge of:
- Machine Learning concepts (supervised/unsupervised learning)
- NLP / LLM use cases (chatbots, summarization, classification)
- Predictive & descriptive analytics
- Agentic AI frameworks
- AI‑assisted SDLC tools
- Experience working alongside:
- Data scientists
- AI engineers
- Analytics teams
- Ability to translate AI outputs into business‑friendly insights.
Note: Hands‑on model building is not mandatory, but conceptual clarity and applied usage is expected.
Nice‑to‑Have Skills
- Familiarity with:
- Cloud platforms (AWS / Azure / GCP – AI services)
- Data platforms (Snowflake, Databricks, BigQuery)
- BFSI / regulated domain exposure (Risk, Ops, Compliance, Customer Analytics).
Key Success Factors
- Ability to bridge business intent and AI capability
- Strong analytical and storytelling mindset
- Comfort operating in ambiguity and emerging AI use cases
Outcome‑oriented thinking (business value > technology)
Role Summary
Looking for a Business Analyst with AI exposure who can act as a strong bridge between business stakeholders, data science / AI teams, and engineering. The ideal candidate will bring core BA skills (requirements, process modeling, stakeholder management) along with hands-on or working knowledge of AI/ML, analytics, and automation-driven use cases.
This role supports AI‑enabled transformation initiatives, including process automation, intelligent decisioning, predictive analytics, and agentic‑assisted workflows.
Key Responsibilities
Business & Functional Analysis
- Elicit, analyze, and document business, functional, and non-functional requirements for AI‑enabled solutions.
- Work closely with business stakeholders to identify AI / analytics use cases that improve efficiency, productivity, risk control, or customer experience.
- Translate business problems into AI problem statements (e.g., prediction, classification, recommendation, NLP use cases).
- Create process flows, user journeys, use cases, and acceptance criteria for AI-driven capabilities.
- Support UAT, validation of AI outputs, and business sign‑off.
AI / Data Collaboration
- Act as a liaison between business, data science, and engineering teams.
- Support definition of:
- Training data requirements
- Data quality rules
- Model assumptions and constraints
- Assist in interpreting model outputs, confidence scores, explainability (XAI), and business impacts.
- Partner with teams delivering ML models, LLM‑based solutions and agentic AI capabilities.
Process & Automation Enablement
- Support automation initiatives by mapping as‑is / to‑be processes and measuring productivity gains.
- Track benefits such as cost optimization, cycle time reduction, productivity uplift, and quality improvements.
Governance & Documentation
- Ensure compliance with data privacy, model governance, and regulatory expectations (especially in BFSI environments).
- Maintain structured documentation:
- BRDs / FRDs
- AI use‑case canvases
- Data dictionaries
- Model impact assessments
Required Skills & Experience
Core BA Skills
- 10 years of experience as a Business Analyst / Techno‑Functional BA.
- Strong experience in:
- Requirements gathering
- Stakeholder management
- Agile delivery (user stories, backlog grooming)
AI / Analytics Exposure (Must‑Have)
- Working knowledge of:
- Machine Learning concepts (supervised/unsupervised learning)
- NLP / LLM use cases (chatbots, summarization, classification)
- Predictive & descriptive analytics
- Agentic AI frameworks
- AI‑assisted SDLC tools
- Experience working alongside:
- Data scientists
- AI engineers
- Analytics teams
- Ability to translate AI outputs into business‑friendly insights.
Note: Hands‑on model building is not mandatory, but conceptual clarity and applied usage is expected.
Nice‑to‑Have Skills
- Familiarity with:
- Cloud platforms (AWS / Azure / GCP – AI services)
- Data platforms (Snowflake, Databricks, BigQuery)
- BFSI / regulated domain exposure (Risk, Ops, Compliance, Customer Analytics).
Key Success Factors
- Ability to bridge business intent and AI capability
- Strong analytical and storytelling mindset
- Comfort operating in ambiguity and emerging AI use cases
Outcome‑oriented thinking (business value > technology)