Lead/Sr. Lead Data Science & AI

4 Months ago • All levels

Data Analysis

Job Description

As a Director, Data Science & AI, you will be responsible for formulating business problems into data science solutions, validating OKRs against statistical measures, and performing data wrangling. You will be expected to excel in data storytelling, problem-solving, and possess strong business acumen. Key responsibilities include autonomous task execution across platforms, making informed decisions, optimizing processes through real-time analytics, managing knowledge, interacting with users, and continuously learning. The role requires proficiency in Natural Language Understanding and Generation, autonomous workflow management, API integration, real-time analytics, and reinforcement learning. Preferred knowledge domains include business operations, customer service, technical documentation, and organizational policies. Performance will be measured by task completion rates, decision quality, user satisfaction, and learning adaptability.

Good To Have:

Business operations (sales, marketing, HR, finance)
Customer service and support
Technical documentation and product knowledge
Organizational policies and compliance frameworks
Excel VBA
Data Curiosity
Technical Decision Making
Communication & Articulation: Vocal & Written
Business Acumen
Design Thinking
Data Literacy

Must Have:

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
Autonomous Task Execution
Decision-Making
Process Optimization
Knowledge Management
Customer Interaction
Learning & Adaptation
Compliance & Ethics
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
Problem Formulation
Data Wrangling
Data Storytelling & Insight Generation
Problem Solving

Add these skills to join the top 1% applicants for this job

team-management

excel

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data-science

reinforcement-learning

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