Job Description

Lead/Sr. Lead Data Science & AI - R01549779

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Director, Data Science & AI

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