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CV Data scientist

Company:
Ford Global Business Services
Location: Chennai, Tamil Nadu
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Job Description & Qualifications

Key Roles and Responsibilities of Position:
  • Applying various Deep learning networks, statistical techniques, explore and experiment on new models through research papers or via various frameworks
  • Understanding, transforming large scale data to usable form for modelling, filtering data with generalization for later use, Cross-validating models for the requirements.
  • Recommend and justify the algorithms to implement for the problems at-hand
  • Implement libraries, algorithms, and tools for processing Lidar data to push the state-of-the-art 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 multi-sensor modalities such as one or more cameras, radars, and Lidar sensors.
  • Perform multi-target tracking through the lifecycle of tracked objects including creation, splitting and merging, and termination of tracked objects.
  • Enhance deep learning networks with multi-GPU and multi-node capabilities
  • Interact with internal stakeholders to understand the business problems
  • Applying calculus, algebra and other math to build reliable, scalable model.
  • Automate algorithms in production through standardization of process and authoring best practices
  • Producing and disseminating technical and non-technical reports that detail the successes and limitations of each project.

Auto req ID

41841BR

Entity/Organization

FGBS- Ford Global Business Services

Area of professional exposure (technical skills)

  • 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
  • Excellent problem solving, communication, and data presentation skills

Education Qualifications

B.E., B.Tech., MBA

No. of years of experience

5 to 8 years

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