Who we are:

Geotab is a global leader in IoT and connected transportation and certified “Great Place to Work.” We are a company of diverse and talented individuals who work together to help businesses grow and succeed, and increase the safety and sustainability of our communities.

Geotab is advancing security, connecting commercial vehicles to the internet and providing web-based analytics to help customers better manage their fleets. Geotab’s open platform and Marketplace, offering hundreds of third-party solution options, allows both small and large businesses to automate operations by integrating vehicle data with their other data assets. Processing billions of data points a day, Geotab leverages data analytics and machine learning to improve productivity, optimize fleets through the reduction of fuel consumption, enhance driver safety and achieve strong compliance to regulatory changes.

Our team is growing and we’re looking for people who follow their passion, think differently and want to make an impact. Ours is a fast paced, ever changing environment. Geotabbers accept that challenge and are willing to take on new tasks and activities - ones that may not always be described in the initial job description. Join us for a fulfilling career with opportunities to innovate, great benefits, and our fun and inclusive work culture. Reach your full potential with Geotab. To see what it’s like to be a Geotabber, check out our blog and follow us @InsideGeotab on Instagram, Twitter or Facebook.

Who you are:

We are looking for Data Scientists who can help us discover value out of our rich and large datasets. Our ideal team member will have the mathematical and statistical expertise you’d expect, but a natural curiosity and creative mind that’s not so easy to find. As you mine, interpret and clean our data, we will rely on you to ask questions, connect the dots and uncover opportunities that lie hidden within, all with the ultimate goal of realizing the data’s full potential. You will join a team of data specialists, but will “slice and dice” data using your own methods, creating new visions for the future. If you are a Data Scientist or a Data Science aspirant who knows how to extract meaning from and interpret data - we would like to hear from you!

What you'll do:

As a Data Scientist you will be a crucial part of producing innovative analytics from Geotab’s big data environment. Geotab’s systems process over 40 billion data records per day from over 1.5 million vehicles in 100+ countries across the globe. We believe that there is collective value in the massive amount of data arising from all of our sensors across the globe. And most importantly, we believe this data can be used to improve safety, infrastructure, and productivity for our customers. You will work with our team of data scientists and engineers who have curated our data repository and have created a series of near real-time and historical datasets categorized under Weather, Urban Infrastructure and Location Analytics. You will leverage data mining techniques, Machine Learning, Deep Learning, Neural Networks and other Artificial Intelligence and statistical analysis tools to build models and high quality prediction systems to help our customers make informed and smarter decisions. You will work closely with Data Analysts, Data Engineers, Professional Services, Data Privacy & Governance, firmware as well as other internal departments such as Software Development and Solutions Engineering to achieve these objectives.

Geotab Data Scientists share a common passion for finding simple solutions for complex problems. You do too!

Our Technology Stack :

We leverage the Google Cloud Platform (GCP) as our big-data environment. Most of our development work happens in Python, leveraging various big data and data science tools in GCP (e.g. Dataflow) as well as from the open-source community (e.g. JupyterLab).

Python Modules : numpy, scipy, theano, keras, pytorch, pandas, matplotlib, xgboost, scikit-learn, nltk, seaborn, networkx, osmnx

Big Data Tools : JupyterLab, TensorFlow, Apache Spark , Beam, Dataflow, Airflow

Google BigQuery, Kafka, Kubernetes

PostgreSQL

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