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Instructor (Data Science)

India

Office Location:

Lohegaon, Pune

Experience: 5-10 Years

Max Notice Period: 60 Days

Outstation Candidates Allowed

Role & Responsibilities:

We are currently looking for a Data Engineer + Instructor – Data Mining to join our Computer Science Department. This is a full-time academic role focused on data mining, analytics, and teaching/mentoring students in core data science and engineering topics.

Key Responsibilities:

  • Develop and deliver comprehensive and engaging lectures for the undergraduate "Data Mining", “BigData” and “Data Analytics” courses, covering the full syllabus from foundational concepts to advanced techniques.
  • Instruct students on the complete data lifecycle, including data preprocessing, cleaning, transformation, and feature engineering.
  • Teach the theory, implementation, and evaluation of a wide range of algorithms for Classification, Association rules mining, Clustering and Anomaly Detections.
  • Design and facilitate practical lab sessions and assignments that provide students with hands-on experience using modern data tools and software.
  • Develop and grade assessments, including assignments, projects, and examinations, that effectively measure the Course Learning Objectives (CLOs).
  • Mentor and guide students on projects, encouraging them to work with real-world or benchmark datasets (e.g., from Kaggle).
  • Stay current with the latest advancements, research, and industry trends in data engineering and machine learning to ensure the curriculum remains relevant and cutting-edge.
  • Contribute to the academic and research environment of the department and the university.

Ideal Candidate:

  • A Ph.D. (or a Master's degree with significant, relevant industry experience) in Computer Science, Data Science, Artificial Intelligence, or a closely related field.
  • Demonstrable expertise in the core concepts of data engineering and machine learning as outlined in the syllabus.
  • Strong practical proficiency in Python and its data science ecosystem, specifically Scikit-learn, Pandas, NumPy, and visualization libraries (e.g., Matplotlib, Seaborn).
  • Proven experience in teaching, preferably at the undergraduate level, with an ability to make complex topics accessible and engaging.
  • Excellent communication and interpersonal skills.

Preferred Qualifications:

  • A strong record of academic publications in reputable data mining, machine learning, or AI conferences/journals.
  • Prior industry experience as a Data Scientist, Big Data Engineer, Machine Learning Engineer, or in a similar role.
  • Experience with big data technologies (e.g., Spark, Hadoop) and/or deep learning frameworks (e.g., TensorFlow, PyTorch).
  • Experience in mentoring student teams for data science competitions or hackathons.

Perks, Benefits and Work Culture

  • Competitive salary packages aligned with industry standards.
  • Access to state-of-the-art labs and classroom facilities.
  • Contribution to cutting-edge academic work in collaboration with the Newton School of Technology and Rishihood University, Sonipat, and Ajeenkya DY Patil University

Job Type: Full-time

Pay: ₹3,800,000.00 - ₹5,000,000.00 per year

Application Question(s):

  • Strong Data Science / Machine Learning Profile
  • Mandatory (Experience) – Must have 5+ years in Data Science and Data Engineering, with expertise in data preprocessing, cleaning, transformation, and feature engineering.
  • Mandatory (Skills 1) – Strong practical proficiency in Python and its data science ecosystem, including Scikit-learn, Pandas, NumPy, and visualization libraries such as Matplotlib and Seaborn.
  • Mandatory (Skills 2) - In-depth knowledge of machine learning algorithms, including classification, clustering, association rule mining, anomaly detection, and experience in implementing and evaluating models.
  • Preferred (Skills) – Prior experience in mentoring student teams for data science competitions or hackathons is preferred
  • Do you have any teaching/tutoring experience, If yes, please share details?

Work Location: In person

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