Sr. Consultant, Data Science (Telematics)

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

As a team member in the Finance and Internal Audit department at Nationwide, the opportunities are endless! You can grow and learn in diverse areas across many disciplines such as Advanced Analytics, Investments, Actuarial, Accounting, Risk Management, Critical Business Advisor and so much more. Let Nationwide help create your career journey!

*Please note, this position may be filled at a lower level depending on qualified candidate skills, and experience.

*This position will offer flexibility of work in office or work from home locations, depending on candidate skills, experience and willingness to travel.

Telematics Data Scientist

Nationwide is seeking a passionate individual with experience working in machine learning, geospatial analysis, and mathematical optimization, to join the Enterprise Analytics Office (EAO) Predictive Analytics team as we embark on an exciting initiative to build our next-generation computing infrastructure to help us enhance our ability to predict driver behavior through the use of telematics.

The ideal candidate should have:

• Bachelor's or above in Industrial Engineering, Operations Research, Systems Engineering, Data Science, Computer Science, Computer/Software Engineering, Civil Engineering, Earth/Atmospheric Sciences, Geographic Information Sciences (GIS), Agricultural Economics, Physics, or other related quantitative field
• Experience working in the areas of vehicle telematics, logistics, pattern detection in sensor or smartphone-connected data, and geospatial mapping
• Experience with the development of computational algorithms to reduce computation time (e.g. MapReduce). Background in spatial optimization algorithms is a major plus.
• Experience with R, Python, or similar programming languages. Deep expertise with relevant geospatial packages (e.g. geopandas and rasterio in Python; maptools, spdep, or OpenStreetMap) is a major plus.
• Experience with popular machine learning and deep learning frameworks (e.g. H2O, TensorFlow, PySpark, PyTorch, MXNet, Caffe)
• Experience working in collaborative, team-based, multidisciplinary environments
• Additional desirable qualifications include:
• Experience with distributed storage and database platforms
• Experience working with weather and atmospheric data
• Experience with batch, micro-batch, streaming, and distributed processing platforms such as Flink, Hadoop, Kafka, Spark, Hudi, AWS EMR, Arrow, or Storm
• Experience working within Amazon Web Services (AWS) cloud computing environments
• Experience with terabytes, petabytes, or even exabytes of data
• Familiarity with containerization tools such as Docker and Kubernetes
• The candidate must be willing to work with a diverse array of partners, including modelers, data engineers, data architects, business professionals, and executives. Because of this, the ability to communicate highly technical material in a friendly, consumable manner is essential.

Compensation Grade:H4