Faculty For Data Engineering & Machine Learning

Position: Assistant Professor/Associate Professor/Professor

Track: Data Engineering & Machine Learning

Location: Sonepat, NCR of Delhi.

Mode: Full Time; On-Campus

Programme: B. Tech CS and Data Science

ABOUT US

Rishihood University

Rishihood University (RU) has been established under The Haryana Private Universities (Amendment) Act, 2020 and is empowered to award degree as specified in section 22 of UGC Act, 1956.

Rishihood University is India’s first and only impact university. ‘Impact’ is the living spirit of Rishihood. The purpose of education envisioned by the thought leaders of our civilization and that which has motivated the founders to build Rishihood University is beyond just awarding degrees and jobs. The purpose of education is to achieve the highest potential in a learner i.e., Rishihood. Rishihood University provides a unique mix of globally relevant education that is rooted in Indian ideas, quality education that is affordable, and a multi-disciplinary exposure with cutting edge skills of a specialist. To achieve this outcome, education cannot be limited to within the classrooms. RU is a fully residential campus where living and learning seamlessly integrate throughout the day. RU faculty and learners have an active participation with society, industry, researchers, entrepreneurs, and policy makers. This keeps the learning at RU focused on solving the biggest challenges faced by humanity and prepares our learners for the real world. It is time India builds universities driven by a higher purpose, that have a strong committed board to back it, that redefine the way education is imparted both within and outside the classroom. Rishihood is a bold initiative to fulfill this idea. Hence, we are looking for like-minded founding faculty members at Rishihood University.

About Position

Track | The DS Core

Foundations of Data Science • Data Mining and Warehousing • Machine Learning • Supervised Learning

India produces some of the world's best data scientists. Most of them were trained to use tools. We want to train students who can build the tools and understand deeply why they work. That is the difference between an execution hub and a technology defining nation. This track is where that shift starts.

You will join Rishihood as a Faculty at the heart of what makes this a Data Science programme and not just another CS degree with a few ML electives tagged on. The DS Core takes students from raw data to predictive intelligence. As you grow into the Track Lead role, you will own this domain entirely: designing the learning arc from data foundations to production grade ML, building the faculty team, and holding the standard of every student who graduates from it.

Requirements

Key Responsibilities

1. Teach Foundations of Data Science, Data Mining and Warehousing, Machine Learning, and Supervised Learning

2. Run labs that feel like real professional work: ETL pipelines with messy datasets, models that students deploy rather than just submit as notebooks

3. Guide capstone projects with rigorous validation: K fold cross validation, A/B testing, and production readiness checks

4. Lead MLOps integration into the curriculum and run seminars on data governance, GDPR, DPDP, and algorithmic fairness

5. Own curriculum design, faculty hiring, and the output standard for this track as Track Lead

Job Specifications

You should hold an MTech, PhD, or equivalent in a relevant field. Beyond the degree, here is what we actually care about.

Technical Depth

• Expert knowledge of supervised learning (Decision Trees, SVM, Random Forest, XGBoost, linear and logistic regression) and data engineering (Star and Snowflake schemas, ETL orchestration, Data Lakes vs Warehouses)
  
• Python data science stack: scikit learn, Pandas, NumPy, advanced SQL; model evaluation at depth: Precision Recall, F1 Score, ROC AUC, cross validation
  
• Working understanding of MLOps in practice and familiarity with cloud ML deployment on AWS SageMaker, Azure ML Studio, or GCP
  

The Person

• You care about data quality as much as model sophistication, correct someone's thinking not just their code, and always ask whether it actually works when deployed
  
• You can translate real industry experience into classroom substance that students recognize as genuine
  

Good to Have

• Industry experience as a Senior Data Engineer, ML Engineer, or ML Architect in a product based company
  
• Strong Kaggle ranking or open source ML contributions; experience deploying ML models at scale in cloud environments
  

This job description is not intended to be all-inclusive. The employee may be expected to perform other duties as assigned by the supervisor.