The SkilD faculty

Learn from the people who build data and AI. Or teach beside them.

The SkilD faculty is a community of industry experts, academicians and advisors from Mastercard, Paytm and enterprise data teams. They teach live, review every build, and shape what SkilD teaches next.

Join the faculty→

Learning, not teaching? See the programs they teach

01The boards

Two boards, one standard: we teach what we have built.

The academic board teaches. The advisory board sets the direction.

Academic board

Teaches live, reviews every build, and guides learners from the first week to the end-to-end project.

Dipandhu Shekhar
Dipandhu ShekharMastercardManager, Data Engineering, Mastercard · 14 years
  • Big data and ETL at scale
  • AWS, end to end
  • Built: Ran the migration of a card-payments reporting pipeline to AWS
Devesh Pandey
Devesh PandeyPaytmSenior Tech Lead, Paytm · 11 years
  • Machine learning and AI in production
  • Generative AI and LLM applications
  • Built: Shipped a fraud-scoring model that screens transactions as they happen
Amit Mishra
Amit MishraLead Data Engineering Trainer · 15 years
  • Big data platforms
  • Cloud data engineering
  • Built: Built the Spark and Kafka training labs used across corporate cohorts
Ashok Padmanabhan
Ashok PadmanabhanCorporate Consultant and Trainer · 20 years
  • ETL design
  • Data architecture and modelling
  • Built: Designed the data warehouse behind a retail chain's daily sales reporting

Advisory board Sample profiles

Senior leaders who keep the programs in step with the industry and with hiring.

Rajiv Menon
Rajiv MenonFormer Head of Data, a large private bank · 25 years
  • Curriculum direction: Which skills SkilD teaches next, and when an old one comes out.
Dr. Lata Krishnan
Dr. Lata KrishnanProfessor of Computer Science · 30 years
  • Academic standards: How every program is reviewed, and what a finished build must show.
Sanjay Kulkarni
Sanjay KulkarniCTO, a SaaS company · 22 years
  • Hiring and industry links: What hiring managers look for this year, and where our learners get seen.
02The mix

Young, seasoned, and everyone in between.

A good room needs both: the people who built the first data warehouses, and the people shipping AI agents this quarter. Between them, every skill we teach is taught by someone who does it at work.

60years of data and AI work on the academic board
11–20years in industry, each
Data engineering
SQLPythonSparkKafkaAirflowdbtAWS · Azure · Google Cloud
Machine learning and AI
Machine learningDeep learningNLPModel deployment
GenAI and agents
LLM applicationsRAGAI agentsMLOps and LLMOps
AI at work
No-code AI toolsAutomation with n8nExcel and Power BI with AI
Where we are adding experts next
03Ways to join

Four ways to bring your experience to SkilD.

Weekend sessions, live

Part-time faculty

Teach live sessions in your field, take doubts in the cohort chat, and review builds, alongside your day job.

One session

Guest lecturer

Take one session on something you built: the architecture, the trade-offs and what broke. Learners ask, you answer.

A few hours a week

Mentor and project reviewer

Review builds against shared standards, guide learners 1-on-1, and get end-to-end projects ready for a hiring panel.

A review each quarter

Advisor

Shape what SkilD teaches next: review the programs with us, bring the hiring view, and point to where the industry is going.

04Why experts join

What teaching at SkilD gives back.

Paid, and part-timeEvery teaching and mentoring role is paid, and each one fits around a full-time job.
A room of peersWork beside industry experts and academicians from data and AI teams, and learn from them as much as you teach.
Your name on the workYour profile on this page, your sessions credited by name, and learners you trained across the industry.
First look at the talentMeet learners at the end-to-end project stage, before anyone else gets to hire them.
05How joining works

From your application to your first session.

Join the faculty

Built it at production depth? Teach it at SkilD.

Tell us what you build and how you’d like to be involved.

  1. 01
    ApplyTwo minutes: your field, your LinkedIn and the role you want.
  2. 02
    A conversationA call with our academic board about what you have built and what you would teach.
  3. 03
    A trial sessionTeach or review one piece live, with our faculty in the room.
  4. 04
    Join the boardYour profile goes on this page, and your first session goes on the calendar.
06Asked, answered

SkilD faculty: your questions, answered.

For experts
Is teaching at SkilD paid?

Yes. Faculty, guest lecturers and mentors are paid for each role, and the terms come to you in writing after the trial session.

Can I teach at SkilD alongside a full-time job?

Yes. Classes are live weekend sessions, and mentoring fits around your week.

Do I need teaching experience to join?

No. You need to have built what you would teach, in production. The trial session is where we work on the teaching together.

What does the SkilD advisory board do?

It reviews what SkilD teaches and what comes next, brings the hiring view, and keeps the programs in step as the industry changes.

For learners
Who teaches at SkilD?

The academic board on this page: industry experts and academicians from data and AI teams, teaching every class of the PG Programs live.

Can I meet the SkilD faculty before I apply?

Yes, at a free live build: ninety minutes, live and free, hosted by our faculty. Save a seat →

Are SkilD classes taught live, or recorded?

Live, with the chat open. Every class is also recorded and up within 24 hours.

For learners

Learn from them, live.

Three PG Programs and their courses, taught by the academic board on this page.

See the programs→

Here to teach? Apply to join the faculty.

Job alerts and The SkilD Brief

Data and AI openings on WhatsApp, and the hiring market twice a month.

The roles the companies our learners work at are hiring for, and the dates of the next free live build. Read three issues of the Brief and you’ll know whether we’re your kind of school.