AI Data Scientist

Key Skills

Deep Learning, English Proficiency, Keras software library, Machine learning techniques, NumPy, Pandas, PyTorch, Reinforcement Learning, Scikit-learn, TensorFlow

Job Description

At Ford Motor Company, we believe freedom of movement drives human progress. We also believe in providing you with the freedom to define and realize your dreams. With our incredible plans for the future of mobility, we have a wide variety of opportunities for you to accelerate your career potential as you help us define tomorrow’s transportation.

Creating the future of smart mobility requires the highly intelligent use of data, metrics, and analytics. That’s where you can make an impact as part of our Global Data Insight & Analytics team. We are the trusted advisers that enable Ford to clearly see business conditions, customer needs, and the competitive landscape. With our support, key decision-makers can act in meaningful, positive ways. Join us and use your data expertise and analytical skills to drive evidence-based, timely decision-making.

Potential candidates should have excellent depth and breadth of knowledge in machine learning, data mining, and statistical modeling. They should possess the ability to translate a business problem into an analytical problem, identify the relevant data sets needed for addressing the analytical problem, recommend, implement, and validate the best suited analytical algorithm(s), and generate/deliver insights to stakeholders. Candidates are expected to regularly refer to research papers and be at the cutting-edge with respect to algorithms, tools, and techniques. The role is that of an individual contributor; however the candidate is expected to work in project teams of 2 to 3 people and interact with Business partners on regular basis.

RESPONSIBILITIES

• 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.

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.