ISBR Business School
Website:
isbr.in
Job details:
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.
KEY 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
MANDATORY QUALIFICATIONS & EXPERIENCE
- 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
PREFERRED / HIGHLY DESIRABLE CRITERIA
- 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
CORE COMPETENCIES & LEADERSHIP ATTRIBUTES
Competency
Technical Depth
Maintains current, hands-on proficiency — writes code, builds models, and solves data problems personally, not just conceptually
Pedagogical Clarity
Translates complex algorithms and statistical concepts into intuitive, business-relevant explanations without dumbing them down
Project Facilitation
Designs and manages messy, real-world analytics projects; comfortable with ambiguity and iterative problem-solving
Industry Currency
Tracks ML/AI tooling changes obsessively; updates curriculum each trimester — not annually
Tool Proficiency
Personally expert in Python, SQL, and BI tools; can teach and evaluate students hands-on in lab sessions
Curiosity
Intellectually driven by data and what it reveals; models the analytical mindset for students continuously
Student Mentorship
Invests in individual students' technical development; particularly attentive to those from non-engineering backgrounds
Cross-sector Awareness
Helps students articulate analytics value in BFSI, FMCG, and healthcare — not only in tech companies
Communication of Complexity
Teaches students to communicate data insights to non-technical stakeholders — a critical and often missing skill
Innovation
Experiments continuously with new tools, datasets, and teaching formats; keeps the lab environment dynamic
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