R&D Quantitative Analyst

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Job Title:
R&D Quantitative Analyst
Ford Motor Company

Job Description
Position Overview/Description:
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 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. Past and current projects include: quantitative marketing analytics; application of statistical / econometric / machine learning techniques to economic, financial, and marketing related business problems; social network analysis; quantitative financial risk management and portfolio optimization; and economic scenario simulation and analysis.

  • Research and apply the latest quantitative techniques toward the solution of important problems from many areas of the automotive and mobility industry
  • Review academic / industry literature, develop and test models, and gain insights from research findings
  • Translate ideas and theory into solutions from which the business can grow
  • 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

Job Requirements:

Basic Qualifications:
  • Master's Degree
  • 3+ years of experience (including time spent during graduate school) doing quantitative research in any of the following areas: 1) advanced econometrics, statistics, time series analysis, and/or forecasting, 2) machine / statistical learning, 3) optimization / mathematical programming, 4) quantitative marketing, 5) quantitative macroeconomic forecasting, simulation, and scenario analysis.
  • 2+ years of experience in at least two of the following languages: R, Python, MATLAB, Java, C/C++/C#, SAS

Preferred Qualifications:

  • Ph.D. in a quantitative field such as Statistics, Economics, Mathematics, Physics, 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 / statistical learning to large 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
The distance between imagination and … creation. It can be measured in years of innovation, or in moments of brilliance. When you join the Ford team; discover all the benefits, rewards and development opportunities you’d expect from a diverse global leader. You’ll become part of a team that is already leading the way, with ingenious solutions and attainable products – and it is always ready to go further.

Visa sponsorship may be available for this position. 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.

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.