ISBR Business School, Bangalore
Website:
isbr.in
Company:
https://www.linkedin.com/company/isbr-business-school-bangalore
Seniority: Director
Industries: Higher Education and Education
Job details:
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.
Click on Apply to know more.
