Toppr is India’s best learning app that provides personalized learning for students studying for boards, Olympiads and various engineering and medical competitive exams. Toppr enables students to study comprehensively and perform at their best in CBSE, ICSE, State Boards along with JEE, NEET and other competitive exams. Toppr was founded in 2013 and is backed by Saif Partners, Helion Ventures and Fidelity Growth Partners (Eight Road Ventures). Based out of Mumbai, Toppr currently caters to 20L+ registered students in classes 5-12, who are taking advantage of its deep-structured content and powerful adaptive algorithms. Toppr has experienced phenomenal top line growth (400% over the last year) at a significant contribution margin, making it one of the few sustainable start-ups in the Indian ecosystem. The company has sales offices in 15 cities from where students can get access to further counselling assistance. The aim is to double the city presence by the end of 2017.
Toppr is looking for a Data Scientist with exceptional analytical, problem solving and communication skills. You would be contributing to solve real business problems in the short-term and long-term goal of the company.
• Carry out research independently and develop advanced algorithms which can then be used to solve problems of large dimensional.
• Dig into the large data sets using data mining techniques like hypothesis testing, machine learning, and retrieval processes.
• Synthesise diverse, complex information and develop innovative analytic frameworks that will impact the business positively.
• Candidates from Tier 1 or Tier 2 colleges will be given preference.
• Should have worked as a Data Scientist for minimum 2 years.
• Hands on experience with any statistical analysis environments such as R, Python, MATLAB, SPSS or SAS and comfortable with relational and non-relational databases.
• Should have used deep learning, machine learning and analytical techniques to create scalable solutions for business problems.
• Hands on experience on 2 or 3 machine learning modeling concepts such a Regression, Clustering, Bayesian Nets, Decision Trees, Recommender System etc would be required.
• Interaction with external or internal stakeholders to directly understand the business problem, help and aid them in the implementation of ML algorithms to solve problems.