Lead/Sr. Lead Data Science & AI - R01549779

Director, Data Science & AI

• Problem Formulation (Business problem to Data Science Problem), OKR Validation against statistical measures, Data Wrangling, Data Storytelling & Insight Generation, Problem Solving, Excel VBA, Data Curiosity, Technical Decision Making (How many iterations to go for vs when to stop iterating), Communication & Articulation: Vocal & Written, Business Acumen (Consume new domains quickly to learn through data), Design Thinking, Data Literacy

• Data Science Foundation: Senior Data Scientist

• Mandatory skills
• • LLM application development and fine-tuning
• • Agentic AI frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel)
• • RAG patterns and enterprise knowledge integration
• • Prompt design, tool integration, and orchestration
• Roles and Responsibilities
• Key Responsibilities
• Autonomous Task Execution: Initiate and complete multi-step tasks across platforms (e.g., CRM, ERP, cloud services).
• Decision-Making: Analyze data and make informed decisions aligned with organizational goals.
• Process Optimization: Identify inefficiencies and propose improvements using real-time analytics.
• Knowledge Management: Research, synthesize, and present information to support strategic initiatives.
• Customer Interaction: Engage with users via chat, email, or voice to resolve queries and escalate issues when necessary.
• Learning & Adaptation: Continuously improve performance based on feedback and outcomes.
• Compliance & Ethics: Operate within defined ethical, legal, and organizational boundaries.
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• 🧩 Required Capabilities
• Natural Language Understanding and Generation
• Autonomous Workflow Management
• Integration with APIs, databases, and enterprise tools
• Real-time analytics and decision-making
• Reinforcement learning or feedback-based adaptation
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• 📚 Preferred Training Data / Knowledge Domains
• Business operations (sales, marketing, HR, finance)
• Customer service and support
• Technical documentation and product knowledge
• Organizational policies and compliance frameworks
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• 📈 Performance Metrics
• Task completion rate and accuracy
• Decision quality and impact
• User satisfaction scores
• Learning velocity and adaptability
• Ethical compliance rate