In this role, you will contribute on multiple levels including: data preparation, algorithm development, documentation, programming, project management, conducting experiments, and interacting with clients.
In this role, the selected candidate will develop and implement mixed integer programming formulations and computationally efficient methods for obtaining optimal or near-optimal solutions. Design and implement heuristic algorithms for obtaining feasible solutions quickly.
In this role, the selected candidate will identify actionable insights, suggest recommendations, and influence the direction of the business by effectively communicating results to cross functional groups.
In this role, the selected candidate will lead the design, implementation, and operation of a state-of-the-art big data analytics approach which is scalable and innovative in the way it extracts, manages and analyzes data.
In this role, you will research and improve on existing forecast models of expected credit and residual risk; execute both descriptive and inferential ad hoc requests in a timely manner
Communicate and present models to business customers and executives.
The selected candidate will be responsible for the discovery, profiling and governance of new and existing data sources. As trusted advisors within GDIA, we help identify untapped opportunities in data, which are then converted into profitable growth
In this role, you will communicate compliance requirements to other functional areas, including Supply Chain, Material Innovation & Exploration, New Product Development & Launch, Product Life Cycle Management, and Global Sustainability.
In this role, the selected candidate will provide constructive input into defining and implementing GDI&A's (Global data and Insights) data governance policies and principles, develop a good understanding of access controls in accordance with Internal Controls and IT Security Policy guidelines.
The Data Ops Analyst role resides within the Ford GDIA organization which was formed to support Ford's Data Acquisition strategy, responsible for providing the data support for enterprise data integration tasks, including ingestion, standardization, enrichment, mastering and assembly of data products for downstream applications.