Inviting applications for the role of Principal Consultant - Business Analyst

In this role, you will act as a critical bridge between business stakeholders, data science teams, and engineering functions. This role focuses on translating business problems into AI‑ready problem statements, defining measurable success criteria, and ensuring AI solutions deliver tangible, ethical, and scalable business impact.

The BA will work closely with Product Owners, Data Scientists, ML Engineers, and Architects across the full AI lifecycle-from use‑case ideation and data readiness to model validation, deployment, and value realization.

Responsibilities:
Business & Use‑Case Definition

Partner with business stakeholders to identify, shape, and prioritize AI/ML/GenAI use cases aligned with strategic goals

Convert ambiguous business problems into clear AI‑ready problem statements and hypotheses

Define business KPIs, success metrics, and ROI measures for AI initiatives

Support feasibility assessments considering data availability, model complexity, and risk

Requirements & Analysis

Elicit and document functional, non‑functional, and data requirements for AI/ML solutions

Define model outcome expectations (accuracy, precision/recall, latency, explainability, confidence thresholds)

Document data requirements (sources, quality, volume, lineage, labeling assumptions)

Create BRDs, FRDs, user stories, acceptance criteria, and AI‑specific requirement artifacts

AI Delivery & Collaboration

Work closely with Data Scientists, ML Engineers, and GenAI solution teams to refine use‑case assumptions

Participate in model review discussions to validate alignment with business intent

Support prompt engineering discussions for GenAI use cases by defining business context and constraints

Assist in trade‑off decisions between model performance, cost, time‑to‑market, and explainability

Validation, Testing & Adoption

Define business‑driven test scenarios for model validation and user acceptance

Validate AI outputs against defined business expectations and compliance requirements

Support user training, change management, and adoption planning for AI‑enabled solutions

Governance & Responsible AI

Ensure adherence to Responsible AI principles (fairness, bias, explainability, data privacy)

Participate in model risk assessments, audit reviews, and compliance documentation

Support stakeholder communication with clear narratives around AI decisions and limitations

Qualifications we seek in you!

Minimum Qualifications:

Bachelor's degree in Business Administration, Computer Science, Engineering, or a related field

experience as a Business Analyst in IT / digital transformation projects

Strong expertise in requirements elicitation, stakeholder management, and documentation

Proven ability to define KPIs, metrics, and business value cases

Hands‑on experience working on AI, ML, or GenAI projects (as BA, Product Analyst, or similar role)

Strong understanding of:

Supervised vs unsupervised learning concepts

Model evaluation metrics (accuracy, precision, recall, F1, etc.)

GenAI basics (LLMs, prompts, embeddings, RAG concepts)

Ability to translate business needs into AI‑appropriate requirements, not just traditional software specs

Strong understanding of data structures, quality issues, and data dependencies

Experience working with datasets, data pipelines, or analytics teams

Ability to ask the (coverage, bias, freshness, representativeness)

Excellent communication skills-able to converse fluently with business, technical, and data science teams

Strong facilitation skills for workshops, use‑case ideation sessions, and model review discussions

Preferred Qualifications:

Master's degree or MBA

Advanced AI / GenAI Capabilities

Experience with LLM‑based solutions (chatbots, copilots, summarization, search, recommendation)

Exposure to prompt engineering, RAG pipelines, or agent‑based architectures

Familiarity with AI platforms such as Azure AI, AWS AI/ML, GCP Vertex AI, Databricks, or similar

Delivery & Tooling

Experience working in Agile / SAFe environments for AI delivery

Familiarity with JIRA, Confluence, Miro, or model documentation tools

Understanding of MLOps concepts (model lifecycle, drift, retraining triggers)

Governance & Compliance

Exposure to Responsible AI, data privacy, or model risk governance frameworks

Experience supporting InfoSec, audit, or regulatory reviews in AI contexts

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