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 Qualified 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 energetic

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.

📌 Specialisation Head Data Analytics (Bengaluru)
🏢 ISBR Business School, Bangalore
📍 Bengaluru