• B.Tech/PhD/M.Tech/M.S in Computer Science, Operational Research, Statistics, Applied Mathematics or Natural Sciences with a very clear understanding of probability and statistics, analytical approach to problem solving, and capability to think critically on a diverse array of problems. Publications in peer-reviewed journals will count in your favour.
• Supervised Machine Learning Algorithms: Logistic Regression, Bayesian Approach, Decision Trees, Support Vector Machines. Neural Networks, Ensemble Methods, Feature selection techniques etc. Understanding of advanced algorithms (i.e. Deep Learning) will be good to have.
• Good understanding of Unsupervised and Semi-Supervised Machine Learning Algorithms.
• Optimization Algorithms: Mathematical Programming (Linear/Non-linear techniques), Convex Optimization, Transportation Problem, Vehicle Routing Problem, Facility Location Problem, Queuing Theory, Inventory Management, Forecasting Techniques, etc. Knowledge about meta-heuristics like Genetic Algorithm, Tabu Search, Simulated Annealing, etc would be beneficial.
• Strong programming skills in Python or Java. Good understanding of C++ and R will be appreciated.
• Significant experience with SQL databases, especially experience in handling geographic data like PostGIS on PostgreSQL will be appreciated.
• Experience with NoSQL databases like MongoDB, Elastic Search, Redis or any graph database.
• Some experience with big data tools like Spark, Hadoop is a plus
• Most importantly, an inquisitive mind, an ability for self-learning and a risk appetite for experimentation and failure