- Doctoral Degree
- Master's Degree
The Analytics Research and Development team within Ford’s Global Data, Insight and Analytics (GDI&A) organization is looking for a highly skilled quantitative analyst / data scientist to assist in all levels of problem formulation, model development, evaluation, and deployment. The analyst will have significant autonomy in conducting research and selecting modeling methodologies. You will have the opportunity to work with some of the brightest global subject matter experts that are transforming the automotive and mobility industries.
Analytics R&D is a cross-functional research team within Ford GDI&A that focuses on applying advanced quantitative methods to solve a wide variety of challenging problems across multiple business areas. Examples of past and current projects include: quantitative marketing analytics including large-scale consumer choice modeling and reinforcement learning; application of statistical, econometric, and machine learning techniques to economic and financial analysis; social network analysis; and quantitative financial risk management and portfolio optimization.
Research and apply quantitative techniques from fields such as statistics, econometrics, optimization, and machine / deep learning toward the solution of important business problems from many areas of the automotive and mobility industry
Enable evidence-based decision making by extracting insights from structured and unstructured data sets that are often large and high-dimensional
Collaborate with other teams within GDIA in the model development and delivery process
Identify new and novel data sources and explore their potential use in developing actionable business insights
Explore emerging technologies and analytic solutions for use in quantitative model development
Help maintain and enhance existing models
A Master's degree in a quantitative field such as Statistics, Economics, Mathematics, Physics, Engineering, Operations Research, Computer Science, Quantitative Social Science, Quantitative Marketing, Quantitative Finance
3+ years of experience (including time spent as a graduate student) doing quantitative research in any of the following areas: econometrics, statistics, time series analysis, Bayesian methods, machine / deep learning, optimization / mathematical programming
2+ years of experience in at least one of the following languages: Python, R, Scala, MATLAB, Java, C/C++/C#, SAS
Ph.D. in a quantitative field such as Statistics, Economics, Mathematics, Physics, Engineering, Operations Research, Computer Science, Quantitative Social Science, Quantitative Marketing, Quantitative Finance
1+ year of post-graduate work experience (in a business or post-doc setting) involving complex quantitative modeling and analysis in any of the areas mentioned under Basic Qualifications
Demonstrated skills in applying techniques from econometrics, statistics, and/or machine / deep learning to large, high-dimensional structured and unstructured data sets
Experience with big data technologies such as Spark, Hadoop, Map/Reduce, Hive, etc.
Experience with parallel / grid / GPU computing
Good oral and written communication skills
Comfortable working in an environment where problems are not always well-defined
At Ford Motor Company, the distance between you and an amazing career has never been shorter. Join the Ford team today, and discover the benefits, rewards and development opportunities you’d expect from a diverse global leader.
Candidates for positions with Ford Motor Company must be legally authorized to work in the United States. Verification of employment eligibility will be required at the time of hire. Visa sponsorship may be available for this position.
Ford Motor Company is an equal opportunity employer committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race, religion, color, age, sex, national origin, sexual orientation, gender identity, disability status or protected veteran status. Ford Motor Company also is committed to take affirmative action to employ and advance in employment such persons.