STEM Leads Gen AI Lead

STEM Leads for GenAI Data

Role Overview

The Team Lead for [Maths/Physics/Chemistry/Biology/Coding/Finance] is a pivotal role that bridges deep academic expertise with the practical application of AI fine-tuning. You will be responsible for leading a team of SMEs, setting the gold standard for quality, and acting as the ultimate authority for your subject. Your primary objective is to guide your team in creating and curating the high-quality data that shapes the "brain" of our AI, ensuring its responses are not just correct, but also insightful, well-structured, and tailored to the learner's level.

This is a role for a passionate educator and a meticulous expert who is excited by the prospect of teaching millions at scale through technology.

Profile of the Ideal Candidate

I. Core Responsibilities

1. Team Leadership & Management:

Define project scope and data creation requirements based on inputs from research and engineering teams

Efficiently allocate team members and resources to specific data creation and evaluation projects, optimizing for expertise and workload.

Lead, mentor, and manage a team of Subject Matter Experts, fostering a collaborative and high-performance culture, based on a thorough understanding of team capabilities

Develop and manage team workflows, set clear performance goals, and provide regular, constructive feedback.

Conduct final quality assurance and review of the team's work, ensuring consistency and adherence to the highest standards.

Establish and report on key performance indicators (KPIs) for team productivity, data quality, and project timelines.

Onboard and train new SMEs on our quality guidelines, tools, and fine-tuning methodologies.

1. Subject Matter & Content Strategy:

Act as the ultimate arbiter for content accuracy and quality in [Maths/Physics/Chemistry/Biology/Coding/Finance], from foundational concepts to advanced, post-graduate level topics.

Develop, refine, and enforce a comprehensive quality rubric and style guide for AI responses. This includes standards for accuracy, clarity, depth, step-by-step explanations, and pedagogical effectiveness.

1. AI Model Fine-Tuning & Evaluation:

Collaborate closely with the AI/ML engineering team to translate subject matter nuances into actionable feedback for model improvement.

Analyze the AI model's performance on [Maths/Physics/Chemistry/Biology/Coding/Finance] queries, identifying systemic weaknesses, biases, or recurring error patterns.

Proactively identify and resolve operational bottlenecks or technical issues within the data creation workflow, collaborating with engineering and product teams as needed

Design and oversee the creation of sophisticated evaluation datasets ("exam papers" for the AI) to benchmark model progress and accuracy.

Guide the team in advanced prompt engineering and data creation techniques to address complex, multi-step problems and conceptual questions.

1. Cross-Functional Collaboration:

Serve as the primary point of contact between your subject team and other departments, including Product Management, Analytics, Engineering, and other Subject Leads.

Communicate complex subject-specific challenges and requirements clearly to a non-expert audience (such as tooling and dashboarding needs)

Contribute to the overall strategy for improving the AI's STEM capabilities.

II. Required Qualifications & Experience

• Educational Background:

  A Ph.D. in [Maths/Physics/Chemistry/Biology/Coding/Finance] or a closely related field is strongly preferred. A Master's degree (M.Sc./M.Tech) from a premier institution (e.g., IIT, NIT, IISc) with an exceptional academic record is the minimum requirement.

• Deep Subject Expertise:

  Unassailable, demonstrable expertise across the breadth and depth of your subject, covering syllabi for JEE Mains & Advanced, and major undergraduate/postgraduate curricula.

• Teaching/Academic Experience:

  A minimum of 7-10 years of combined experience in teaching, research, or content development. Significant experience teaching for competitive exams (JEE Mains/Advanced) is a massive plus.

• Leadership Experience:

  Proven experience (2+ years) in a leadership or mentorship role, managing a team of academics, content creators, or researchers.

• Exceptional Communication Skills:

  Flawless written and verbal communication skills. Ability to articulate complex concepts simply and effectively.

III. Preferred Qualifications (The "Ideal" Candidate)

• Ed-Tech Experience:

  Prior experience working in an educational technology company, especially one focused on AI-driven learning.

• Published Work:

  A track record of publications in reputable journals or authorship of acclaimed textbooks or study materials.

• Data-Driven Mindset:

  Comfortable using data and analytics to evaluate content quality and model performance.

IV. Personal Attributes & Soft Skills

• Meticulous & Detail-Oriented:

  An obsession with accuracy and a keen eye for detail. You notice the small errors that others miss.

• Pedagogical Passion:

  A genuine passion for education and a deep understanding of what makes an explanation effective and engaging for a learner.

• Analytical Problem-Solver:

  You can break down complex problemswhether a physics question or a pattern of AI errors—into manageable parts and devise effective solutions.

• Highly Organized:

  Ability to manage multiple priorities and projects in a fast-paced, dynamic environment.

Collaborative & Adaptable: