Ford AI Data Scientist 

Mexico, State of Mexico, Nezahualcóyotl 

98399625

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.

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.

Qualifications:

English proficiency (written and verbal).

Bachelor’s or Post-Graduate degree in Computer Science, Operational research, Statistics, Applied mathematics, or in any other engineering discipline.

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.