Specialisation Head — Data Analytics

ABOUT THE COMPANY

This is the most technically demanding and highest-demand specialisation head role in the institution. The Data Analytics Head must be a genuine practitioner-academic hybrid — someone who has built, deployed, and delivered value from data solutions in industry, and who can now create India's most practically rigorous management-level analytics program. Theory-first applicants need not apply.

ABOUT THE ROLE

To develop industry-ready data analysts and analytics managers who can secure ₹10 LPA+ roles in technology, BFSI, consulting, healthcare, and analytics-first companies — and deliver value from their first week on the job. To build the Data Analytics specialisation as a differentiating institutional asset — the program that serious analytics recruiters specifically seek out.

RESPONSIBILITIES

• Design a hands-on, project-first Data Analytics curriculum — covering Python, R, SQL, statistics, machine learning fundamentals, data visualisation (Tableau/Power BI), and business problem framing
• Structure the entire learning journey around real datasets and business problems — not synthetic textbook exercises
• Build and manage a dedicated Analytics Lab — ensuring students have access to cloud computing environments (AWS/GCP/Azure basics), Jupyter notebooks, and BI tools
• Hire visiting faculty exclusively from active data science, ML, and analytics industry roles — not retired practitioners
• Design a trimester-wise skill progression: SQL and Python fundamentals (T1–2) → advanced analytics and ML (T3–4) → capstone data solution (T5–6)
• Build live project pipelines with technology companies, analytics firms, BFSI organisations, and startups — minimum 4 per trimester
• Develop a Capstone Project framework where every student delivers a complete, real data solution (data ingestion to insight to recommendation) by Term 6
• Prepare students for analytics interviews across all sectors — not only technology; including BFSI, FMCG, healthcare, and operations analytics
• Track AI, GenAI, and machine learning trends — update curriculum every trimester to reflect industry tooling changes
• Coordinate with the Director of Corporate Relations to build analytics recruiter relationships beyond pure technology companies

QUALIFICATIONS

• Master's degree or PhD in Data Science, Statistics, Computer Science, Applied Mathematics, or a quantitative management discipline
• Minimum 8–10 years of experience with at least 5 years in hands-on data analytics, data science, or machine learning roles in industry — not consulting or advisory only
• Hands-on, current proficiency in Python (pandas, scikit-learn, matplotlib), SQL, and at least one BI tool (Tableau or Power BI)
• Experience teaching, training, or mentoring data skills in a structured program — bootcamp, corporate training, or academic setting
• Ability to run and supervise multiple simultaneous student data projects across different business domains

REQUIRED SKILLS

• Industry experience at a recognised analytics-driven organisation — Mu Sigma, Fractal, Tiger Analytics, Flipkart, Amazon, Razorpay, or equivalent
• Certifications: Google Professional Data Engineer, AWS Data Analytics, Databricks Certified Associate, or equivalent
• Exposure to cloud-native data stacks — Spark, dbt, Snowflake, or BigQuery
• Knowledge of GenAI tooling and LLM application in business contexts — increasingly demanded by recruiters
• Publications or conference presentations in data science, AI, or analytics

PREFERRED SKILLS

• Maintains current, hands-on proficiency — writes code, builds models, and solves data problems personally, not just conceptually
• Translates complex algorithms and statistical concepts into intuitive, business-relevant explanations without dumbing them down
• Designs and manages messy, real-world analytics projects; comfortable with ambiguity and iterative problem-solving
• Tracks ML/AI tooling changes obsessively; updates curriculum each trimester — not annually
• Personally expert in Python, SQL, and BI tools; can teach and evaluate students hands-on in lab sessions
• Intellectually driven by data and what it reveals; models the analytical mindset for students continuously
• Invests in individual students' technical development; particularly attentive to those from non-engineering backgrounds
• Helps students articulate analytics value in BFSI, FMCG, and healthcare — not only in tech companies
• Teaches students to communicate data insights to non-technical stakeholders — a critical and often missing skill
• Experiments continuously with new tools, datasets, and teaching formats; keeps the lab environment dynamic

PAY RANGE AND COMPENSATION PACKAGE

Performance will be reviewed trimester-by-trimester against the following institutional outcomes:

• 100% of Data Analytics students placed within 60 days; median CTC ≥ ₹10 LPA
• 100% of students proficient in Python and SQL by end of Term 3 (assessed via structured project evaluation)
• Minimum 4 live analytics projects with companies delivered every trimester
• Every student delivers a complete Capstone Data Project by Term 6 — evaluated by an industry panel
• Minimum 80% of industry evaluators rate capstone projects as 'hire-ready' or above
• Analytics Lab operational with cloud access, Python/R environments, and BI tools before batch Day 10
• Student satisfaction with Data Analytics curriculum ≥ 4.2/5.0 — held to higher standard given premium positioning

EQUAL OPPORTUNITY STATEMENT

We are committed to diversity and inclusivity in our hiring practices and encourage applications from all qualified individuals.