DirecciónAt 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 must demonstrate experienced seniority to effectively oversee and mentor junior Data Scientists within the team.
Minimum requirements
- 5+ years of experience working in a team environment, supporting customer/end users.
- 5+ years of hands-on experience in Python programming for data analysis and machine learning.
- 5+ years of experience implementing and maintaining successful cross functional demand and integration management.
- 5+ years of experience with agile and lean workflow management methodologies (e.g., Kanban)
- 5+ years of experience with project management tools to manage user requirements, stories, and tracking the delivery process (Confluence/Jira or Rally)
- 5+ years of experience with developing and delivering data products to support analytic applications.
- English proficiency (written and verbal).
Preferred Requirements
- 2+ years of experience with Product Driven Organization (PDO) Model
- 2+ years of experience leading a team of Data Scientists.
- 2+ years of experience leading an analytic team in a product owner role.
- 4+ years of hands-on experience with data modeling, data mining, machine learning algorithms or statistical analysis
- Demonstrated success in delivering analytics products, with exposure to machine learning and advanced data analysis techniques.
- Agile/Scrum certification or equivalent experience is a plus.
- Experience with product lifecycle management tools and analytics platforms.
- Excellent leadership, communication, and interpersonal skills. Ability to thrive in a fast-paced, collaborative, and innovative environment.
Ford Motor Company is an Equal Opportunity Employer, as we are committed with a diverse workforce, and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity and/or expression, status as a veteran and basis of disability.
- 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.
