Company Description FACE Prep is one of India's largest placement-focused skill development companies specializing in job preparation for the tech sector. Since 2008, the organization has helped millions of students launch their careers through masterclasses, self-paced learning, and intensive workshops and bootcamps. FACE Prep focuses on building industry-relevant skills that align with top-paying technology roles. Its alumni work across the globe in leading companies such as Google, Microsoft, Meta, Adobe, PayPal, Amazon, TCS, Infosys, Wipro, Thoughtworks, Cognizant, and Accenture. The team is mission-driven and passionate about improving career outcomes for learners.
Role Description This is a full-time, on-site role for a Data Science Faculty (PG Only – Freshers) based in Coimbatore. The faculty member will deliver engaging classroom sessions on core data science topics such as statistics, machine learning, Python programming, data analysis, and visualization. Responsibilities include preparing lesson plans, creating learning materials, designing assignments and assessments, and providing feedback to help learners strengthen concepts and applied skills. The role involves mentoring and guiding postgraduate students, clarifying doubts, and supporting project work and case studies aligned with industry expectations. The faculty member will also collaborate with the academic team to update curriculum, incorporate real-world examples, and participate in internal training to stay current with evolving data science tools and practices.
Qualifications
- Postgraduate degree (M.Tech/M.E./M.Sc./MCA or equivalent) in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative discipline.
- Strong foundation in statistics, probability, linear algebra, and analytical problem-solving.
- Proficiency in Python for data analysis (e.g., NumPy, pandas, matplotlib/seaborn) and basic software development practices.
- Understanding of core machine learning concepts (supervised/unsupervised learning, model evaluation, overfitting, feature engineering).
- Ability to explain complex technical topics clearly to learners with diverse backgrounds, using examples and step-by-step demonstrations.
- Interest in academic instruction, mentoring, and facilitating hands-on learning through projects, labs, and exercises.
- Good written and verbal communication skills in English; comfort presenting to groups and handling interactive sessions.
- Openness to feedback, willingness to learn new tools and frameworks, and ability to adapt teaching methods based on learner needs.
- Any exposure to real-world data projects, internships, Kaggle-style competitions, or academic research in data science is an added advantage.
- Familiarity with SQL, basic data querying, and data visualization tools (e.g., Power BI, Tableau, or
