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
Specialization
Data Science Foundation: Senior Data Scientist
Job requirements
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.
🧩 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
📚 Preferred Training Data / Knowledge Domains
Business operations (sales, marketing, HR, finance)
