Job Description

What\'s the role The Machine Learning team in our Dynamic & Spatial Content Business Unit has identified a number of promising business growth and content quality improvement opportunities. Leveraging HERE\'s extensive data available within our organization, our goal is to further deploy machine learning based algorithms generating insight based on vehicle movement activity to improve DSC\'s current products across the portfolio. In order to accelerate the implementation of these activities, we are growing our team of data scientists. In this position, you will join the team of Master\'s and Ph.D. level data scientists who are driving our modeling efforts to move from real-time to predictive products. Our efforts on quality improvement are currently divided into three areas: Improving the traffic model into longer term and more sophisticated prediction algorithms. As an example, modeling the impact of accidents and roadworks on congestion duration. This, in turn improves, HERE\'s estimates of travel times and resulting improvement in routing algorithms. Leveraging vehicle movement data to gain insight in HERE POI accuracy and activity. Support HERE\'s initiatives around our emerging EVs products and charging points by creating algorithms to predict CP availability. Our efforts on business growth opportunities involve creating analytical models that, for example, generate insight into vehicle volume categories, intersection signal timings, and CO2 emissions. Throughout the years, the team has consistently been one of the highest contributors to HERE\'s I.P portfolio as new opportunities are discovered and you would be expected to participate in those efforts. Who are you A data scientist experienced with using real-world data to analyze and develop algorithms that are incorporated into commercially profitable product lines. Someone who can leverage clustering algorithms to model non-Gaussian statistics who is able to study the physical characteristics of a problem and design mathematical models that solve problems from first principles has a desire to learn, develop, and keep up to pace with the state-of-the-art machine learning based algorithms has the ability to be a subject matter expert on HERE\'s technologies and product lines and leverage that knowledge to identify derivative algorithms to create new growth opportunities. You should also have: Masters or PhD in computational science or engineering with a minimum of 2+ years related, professional work experience. Knowledge of data mining and analytic methods. Perform your day-to-day work by programming in Python and work with associated statistical and analytics packages in python. Leverage AWS technologies and use them to develop modelling pipelines. Proficiency with statistical analysis packages. to effectively interact with both internal and external customer executives, technical and non-technical to explain various use case and how to deploy predictive systems and techniques. Demonstrated proficiency with understanding, specifying, and explaining predictive modeling solutions and collaborating within teams of other data scientists and engineers to execute projects delivering those solutions. HERE is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, age, gender identity, sexual orientation, marital status, parental status, religion, sex, national origin, disability, veteran status, and other legally protected characteristics. Make HERE your destination, we are just getting started! Apply now! Who are we HERE Technologies is a location data and technology platform company. We empower our customers to achieve better outcomes - from helping a city manage its infrastructure or a business optimize its assets to guiding drivers to their destination safely. At HERE we take it upon ourselves to be the change we wish to see. We create solutions that fuel innovation, provide opportunity and foster inclusion to improve people\'s lives. If you are inspired by an open world and driven to create positive change, join us. Learn more about us on our YouTube Channel.

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