Data Scientist - Telematics

August 21

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Tiger Analytics

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AI & Analytics for today’s business challenges.

Machine Learning • Predictive Analytics • Forecasting • Optimization • Natural Language Processing

1001 - 5000

Description

• Apply strong expertise in AI through the use of machine learning, data mining, and information retrieval to design, prototype, and build next generation advanced analytics engines and services. • Collaborate with cross-functional teams and business partners to define the technical problem statement and hypotheses to test. • Develop efficient and accurate analytical models which mimic business decisions and incorporate those models into analytical data products and tools. • Drive current and future strategy by leveraging analytical skills as you ensure business value and communicate the results.

Requirements

• Bachelor’s Degree in Computer Science or closely related field. • 4 to 8 years of experience in Data Science. • Experience with R, Python, or similar programming languages. • 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). • 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). • Understanding of the challenges in data harmonization and feature preparation from variety of 3rd party providers. • Experience with distributed storage and database platforms. • Understanding how the telematics data gets leveraged downstream for BI (trip completion) and analytics purposes (pricing). • 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. • Background in spatial optimization algorithms is a major plus.