Data Scientist

at Ford Motor Company in Springfield, Illinois, United States

Job Description

We are the movers of the world and the makers of the future. We get up every day, roll up our sleeves and build a better world — together. At Ford, we’re all a part of something bigger than ourselves. Are you ready to change the way the world moves?

The Global Data Insights and Analytics (GDI&A) Quality Analytics team is looking for a data scientist with expertise developing and deploying analytics solutions. As a member of this team with varied strengths, you will have the opportunity to work with some of the brightest global domain experts in vehicle quality and vehicle connectivity who are transforming the automobile industry.

Our ideal candidate will lead or assist in AI/ML model development and deployment to support vehicle quality improvement and warranty reduction, by using connected vehicle data and other data sources to predict emerging customer concerns. You will be encouraged to work with AI/ML experts and IT team to choose the right software architecture and implement the AI/ML solution. We are seeking independent problem solvers with a technical mentality who have a passion for learning and applying groundbreaking technologies.

What you’ll do…

You will be responsible for providing the data analytics support for projects, including assembly of data for building models, building data pipelines, and performing predictive/prescriptive analytics, and building POC web applications. More specifically, you will:

Collaborate with Product Development, Manufacturing and Quality experts to understand their requirements and overall business needs and translate high-level ideas into well-defined mathematical problems

Collaborate with IT partners to ensure that the AI/ML solutions are deployed seamlessly

Identify key data elements to extract/decode/analyze. Understand which elements of large data sets can be most effectively demonstrated in the analytic models and what potentially useful information may be missing.

Applying advanced analytical solutions (including machine learning and statistical models), and conduct analysis that consists of problem formulation, data extraction and pre-processing, modeling, validation, ongoing work, and presentations.

Manipulate high-volume, high-dimensional structured and unstructured data from a variety of sources and implement algorithms to identify anomalies, relationships, and trends (preferred)

Collaborate internally and externally to identify new and novel data sources and explore their potential use in