The global data insights and analytics (gdi&a) department at ford motor company is looking for qualified people who can develop scalable solutions to complex real-world problems using machine learning, big data, statistics, econometrics, and optimization. The goal of gdi&a is to drive evidence-based decision making by providing insights from data. Applications for gdi&a include, but are not limited to, connected vehicle, smart mobility, advanced operations, manufacturing, supply chain, logistics, and warranty analytics.
*what you'll need*
- strong english skills (oral, reading, writing, and listening)
- bachelor’s degree or post-graduate degree in computer science, operational research, statistics, applied mathematics, or in any other engineering discipline.
- +4 years of experience in feature engineering, hyper parameter tuning, model evaluation, etc.
- +3 years of experience using pandas/numpy/scikitlearn, pytorch, tensorflow, keras.
*even better if you have*
- experience with containerization (e.g., docker), constructing rest apis using frameworks like flask or fastapi, experience with message brokers (e.g., kafka, rabbitmq), and adherence to best software development practices (e.g., version control, testing).
- familiarity with cloud platforms and services, ideally including experience with google cloud platform (gcp) services such as cloud run, bigquery, cloud sql, pub/sub, compute engine, artifact registry, or related services from other cloud providers (e.g., aws, azure).
*disclaimer*
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.
*what you'll do*
- 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.
- enhance deep learning networks with multi-gpu and multi-node 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 non-technical reports that detail the successes and limitations of each project.