Role Overview

We are building an AI-first education platform where AI directly improves how students learn,get guidance, and succeed.

We are looking for a Head of Data Science/AI/ML who can innovate fast, ship reliably, and stay cost-conscious while scaling. This role is about real-world AI, not research labs or vanity demos.

You will own AI systems that impact millions of students-from voice counsellors and personalized learning to student profiling and success prediction-while ensuring every model, API call, and infra decision justifies its cost.

What You Will Own

- The end-to-end AI charter across Adda Education

- Applied, production-grade AI systems that scale sustainably

- Balancing innovation, accuracy, speed, and cost-always

Key Responsibilities

1. AI Strategy with Cost Discipline

- Define an AI-first roadmap grounded in measurable student and business outcomes

- Evaluate AI ideas not just for capability, but for ROI and long-term cost sustainability

- Make pragmatic build vs buy decisions across models, infra, and tooling

- Avoid over-engineering; ship what works and iterate fast

2. Student Profiling & Personalization at Scale

- Build robust, explainable student profiles using academic, behavioral, and engagement data

- Drive personalization systems across content, practice, assessments, and study plans

- Ensure models scale to millions of learners without runaway infra or API costs

3. AI Counsellors & Voice Systems

Lead development of AI-powered counsellors, including:

- Voice-based AI guidance

- Conversational support systems

- Motivation and intervention engines

- Optimize NLP, speech, and LLM usage for latency, reliability, and cost efficiency

- Blend AI guidance with human counsellor workflows where needed

4. Applied ML & Production Deployment

Own the full ML lifecycle:

- Problem framing

- Feature engineering

- Model training and evaluation

- Deployment, monitoring, and retraining

Implement systems for:

- Model performance tracking

- Drift and bias detection

- Cost and usage monitoring

- Favor simple, robust models where they outperform complex ones

5. Innovation with a Grind Mindset

- Constantly explore new models, techniques, and architectures

- Rapidly prototype, test, discard, and iterate

- Personally stay hands-on when stakes are high or timelines are tight

- Push teams to deliver working systems, not endless experiments

6. Leadership & Culture

- Build and lead teams across data science, ML engineering, and applied AI

Set expectations around:

- Ownership

- Delivery

- Cost awareness

- Create a culture where shipping, learning, and accountability matter more than perfection

Who This Role Is For

- Someone who loves building under constraints

- Comfortable saying "this is too expensive" and finding a smarter alternative

- Energized by fast execution and continuous iteration

- Believes AI should be useful, scalable, and sustainable

Required Experience & Skills

- 10+ years in data science, ML, or applied AI roles

- Proven experience deploying AI systems at scale

Strong expertise in:

- ML, NLP, recommender systems

- LLM-based architectures and agents

- Speech / voice systems (preferred)

- Experience optimizing AI infra, inference costs, and model efficiency

- Strong collaboration skills with product and engineering teams

How Success Will Be Measured

- Students see measurable improvement in outcomes and engagement

- AI systems are reliable, scalable, and cost-controlled

- Personalization becomes a core platform advantage

- Teams ship faster with better technical and cost discipline

Why This Role Is Hard-and Worth It

- High expectations, real-world constraints, no academic buffers

- Decisions you make affect millions of students

- Opportunity to build meaningful AI, not experimental demos

- Direct ownership and visibility at the leadership level