An MS/PhD in Computer Science, Operational research, Statistics, Applied mathematics, or in any other engineering discipline. PhD strongly preferred. Should have experience in feature engineering, hyper parameter tuning, model evaluation etc. Good exposure to machine learning/text mining tools and techniques such as Clustering/classification like SVM, Deep Learning networks like FRCNN, MRCNN, ResNet, FVRCNN, SalsaNext, NASnet, LSTM Reinforcement learning, and other numerical algorithms Should have experience in using Pandas/Numpy/ScikitLearn, Pytorch, Tensorflow, Keras, ROS, Gazebo, OpenJAUS Practical knowledge of automotive sensors like Camera, RADAR etc. Sound knowledge on the Driver assistance systems (feature functions like lane departure prevention, collision avoidance etc.) Strong theoretical knowledge of detection, segmentation, obstacle detection, graphical methods, probabilistic algorithms or optimization Hands on with visualization tools for sensors Knowledge in developing, calibrating and testing multi-sensor systems Applied knowledge of point cloud or Lidar based algorithms such as segmentation, localization, filtering Strong background and understanding of mathematical concepts relating to probabilistic models, conditional probability, numerical methods, linear algebra, neural network under the hood details Familiarity with data science toolkit such as jupyter lab/notebooks, pandas, bash scripting, Linux environment Publications and presentations in recognized (CVPR, NIPS, ECCV, ICML) Machine Learning journals/conferences is a Big plus Familiarity with any one programming (e.g., C/C++/Java) or scripting (R/Python) languages