Data Scientist

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Role Overview:

We are looking for a Business Analyst Lead with a strong grounding in Generative AI to bridge the gap between innovation and business value. In this role, you'll drive adoption of GenAI tools (LLMs, RAG systems, AI agents) across enterprise functions, aligning cutting-edge capabilities with practical, measurable outcomes.

Key Responsibilities:

1. GenAI Strategy & Opportunity Identification

• Collaborate with cross-functional stakeholders to identify high-impact Generative AI use cases (e.g., AI-powered chatbots, content generation, document summarization, synthetic data).
• Lead cost-benefit analyses (e.g., fine-tuning open-source models vs. adopting commercial LLMs like GPT-4 Enterprise).
• Evaluate ROI and adoption feasibility across departments.

2. Requirements Engineering for GenAI Projects

• Define and document both functional and non-functional requirements tailored to GenAI systems:
• Accuracy thresholds (e.g., hallucination rate under 5%)
• Ethical guardrails (e.g., PII redaction, bias mitigation)
• Latency SLAs (e.g., <2 seconds response time)
• Develop prompt engineering guidelines, testing protocols, and iteration workflows.

3. Stakeholder Collaboration & Communication

• Translate technical GenAI concepts into business-friendly language.
• Manage expectations on probabilistic outputs and incorporate validation workflows (e.g., human-in-the-loop review).
• Use storytelling and outcome-driven communication (e.g., “Automated claims triage reduced handling time by 40%.”)

4. Business Analysis & Process Modeling

• Create advanced user story maps for multi-agent workflows (AutoGen, CrewAI).
• Model current and future business processes using BPMN to reflect human-AI collaboration.

5. Tools & Technical Proficiency

• Hands-on experience with LangChain, LlamaIndex for LLM integration.
• Knowledge of vector databases, RAG architectures, LoRA-based fine-tuning.
• Experience using Azure OpenAI Studio, Google Vertex AI, Hugging Face.
• Data validation using SQL and Python; exposure to synthetic data generation tools (e.g., Gretel, Mostly AI).

6. Governance & Performance Monitoring

• Define KPIs for GenAI performance:
• Token cost per interaction
• User trust scores
• Automation rate and model drift tracking
• Support regulatory compliance with audit trails and documentation aligned with EU AI Act and other industry standards.

Required Skills & Experience:

• 7–10 years of experience in business analysis or product ownership, with recent focus on Generative AI or applied ML.
• Strong understanding of the GenAI ecosystem and solution lifecycle from ideation to deployment.
• Experience working closely with data science, engineering, product, and compliance teams.
• Excellent communication and stakeholder management skills, with a focus on enterprise environments.

Preferred Qualifications:

• Certification in Business Analysis (CBAP/PMI-PBA) or AI/ML (e.g., Coursera/Stanford/DeepLearning.ai)
• Familiarity with compliance and AI regulations (GDPR, EU AI Act).
• Experience in BFSI, healthcare, telecom, or other regulated industries.