Ai Data Scientist - Chennai, Tamil Nadu, India - Ford Motor Company

Ford Motor Company

Verified Company

Chennai, Tamil Nadu, India

2 weeks ago

Posted by:

Deepika Kaur

beBee Recuiter

Description

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 multisensor 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

An MS/PhD in Computer Science, Operational research, Statistics, Applied mathematics, or in any other engineering discipline. PhD strongly preferred.

Understanding, transforming large scale data to usable form for modelling, filtering data with generalization for later use, Crossvalidating models for the requirements.
Recommend and justify the algorithms to implement for the problems athand
Implement libraries, algorithms, and tools for processing Lidar data to push the stateoftheart in obstacle detection, object tracking, and related perception challenges
Developing solutions for 3Cs Competitive, Cooperative and Complementing sensor framework projects
Build perception pipeline fusing Camera, LIDAR, RADAR data for 2D and 3D object detection, scene segmentation, classification, tracking, event classification and motion predictions
Research and develop algorithms for sensor fusion and object association across multisensor modalities such as one or more cameras, radars, and Lidar sensors.
Perform multitarget tracking through the lifecycle of tracked objects including creation, splitting and merging, and termination of tracked objects.
Enhance deep learning networks with multiGPU and multinode capabilities
Interact with internal stakeholders to understand the business problems
Automate algorithms in production through standardization of process and authoring best practices
Producing and disseminating technical and nontechnical reports that detail the successes and limitations of each project.