DENSO seeks a Data Analytics Specialist

By | jobs

DENSO is one of the largest global automotive suppliers of advanced technology, systems and components in the areas of thermal, powertrain control, electronics and information and safety. From automated driving to hybrid and electric vehicle components, we’re crafting the core technologies of modern mobility. We draw on the strengths of innovators who are joining together to become an unstoppable force for global good. With our North American headquarters located in Michigan, DENSO employs more than 24,000 people at 28 consolidated subsidiaries and 4 affiliates across the North American region. Worldwide, the company has more than 200 subsidiaries and affiliates in 38 countries and regions and employs more than 170,000 people. Consolidated global sales for the fiscal year ending March 31, 2018, totaled US$48.1 billion.

Primary Responsibilities
The Data Analytics Specialist will be responsible for analyzing V2X data and developing core algorithms for improving traffic safety by interpreting and predicting traffic and driver behavior including:

 Applies statistical techniques and quantitative methods to analyze V2X driving data and traffic data. Utilizes machine learning methods in support of statistical analysis. Assists in seeking, evaluating and implementing numerical methods and applications.
 Aggregate data from multiple sources and assimilates into meaningful inputs for databases. Create new hypotheses derived from analytics for applications towards strategic planning and business development efforts
 Engage with external partners in industry and academia as needed to ensure we maintain technological leadership. Collaborates with members of various data analytic teams.

Education & Experience Desired:

  • PhD degree in, Mathematics/Statistics and/or Computer Science or other technical concentration or an equivalent combination of education. Three to five years of experience in a relevant technical, analytical or statistical role. Experience working with large data sets, and/or experience working with distributed computing tools is desired.
  • Some experience solving V2X analytical problems using quantitative approaches. Familiarity manipulating and analyzing high-volume, high-dimensional V2X data from varying sources.
  • Experience working with various machine learning methods, including supervised and unsupervised learning. Familiarity with relational databases and SQL. Knowledge of an analysis tool such as Python; C/C++; TensorFlow or PyTorch, MatLab/Simulink.

If interested contact Dr. Rajesh Malhan (, Director, NA Research & Development Dept., and provide your employment authorization status (H1B, citizen, etc).

MIDAS Director, H.V. Jagadish, and affiliated faculty Levenstein and Hampshire, awarded NSF grant for data equity

By | News, Research

View video on data ethics.  


U-M receives $2M NSF grant to explore data equity systems

By Alex Piazza

Data science is an important tool that can help researchers tackle important societal challenges ranging from mobility and health to public safety and education.

But data science techniques and technologies also pose enormous potential for harm by reinforcing inequity and leaking private information. As a result, many sensitive datasets are restricted from research use, impeding progress in areas that impact society.

The University of Michigan, with a $2 million grant from the National Science Foundation (NSF), plans to establish a framework for a national institute that would enable research using sensitive data, while preventing misuse and misinterpretation.

“Data science has proven time and time again to be an invaluable resource when addressing emerging challenges and opportunities in areas of broad potential impact,” said H.V. Jagadish, director of the Michigan Institute for Data Science. “But having access to information comes with a great deal of responsibility, so our first priority is to ensure data science is not misused to disproportionately harm underrepresented groups.”

U-M researchers will partner with colleagues at New York University and the University of Washington over the next two years to deploy new techniques and technologies that enable responsible data science, while establishing an interdisciplinary community focused on the study, design, deployment and assessment of equitable data systems.

Equity is an important facet of data science that NSF aims to strengthen in the coming years, as the federal agency partners with universities such as U-M to enable new modes of data-driven discovery that will transform the frontiers of science and engineering.

The centerpiece of its ongoing effort, called Harnessing the Data Revolution at NSF, is the development of national institutes that address multidisciplinary problems in big data. U-M will help lay the groundwork for developing these institutes, which will eventually serve as a point of convergence for researchers from multiple disciplines to share expertise and address pressing challenges in data science.

“Information is being gathered about all of us, from our Google searches and online purchases to property tax records and social media activity,” said Margaret Levenstein, director of the Inter-university Consortium for Political and Social Research at U-M, which maintains the world’s oldest and largest archive of research and instructional data for the social and behavioral sciences. “You would assume the usage of data to be accurate and fair, but that is not always the case. That is why building a framework is so important because, in order for us to harness the enormous potential of big data, we need to ensure equity and privacy.”

H.V. Jagadish (U-M) is the principal investigator on this grant. Robert Hampshire (U-M), Bill Howe (UW), Margaret Levenstein (U-M) and Julia Stoyanovich (NYU) are co-principal investigators.