Lead/Sr. Lead Data Science & AI - R01549779
Team: AI & Data Engineering : Data Science
Location: Bangalore, Karnataka, India
Commitment: Employee
Workplace Type: hybrid
Salary:
Primary Skills
- 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)
- Customer service and support
- Technical documentation and product knowledge
- Organizational policies and compliance frameworks
- 📈 Performance Metrics
- Task completion rate and accuracy
- Decision quality and impact
- User satisfaction scores
- Learning velocity and adaptability
- Ethical compliance rate
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