We are looking for a smart and passionate graduate student with a strong background in machine learning and an interest in human mobility to join our team in Zürich for a 6-month internship at the intersection ofmachine learning, statistics, and computer science for spatio-temporal data.
In this role you are a member of the exceptional Data Science team of Teralytics, consisting of specialists in machine learning, statistics and computer science. You will be working on a cutting-edge R&D topic in human mobility, producing your own innovative solution to the research problem.
You will have the opportunity to manipulate large amounts of anonymised, geo-located human movement data in conjunction with diverse open data sources and proprietary data insights. You will build on top of proficient in-house data processing pipelines and access a high-performance computing cluster. Along the way you will benefit from the guidance and support of our multidisciplinary data science team.
Prototype an algorithm that identifies trip segments by mode of transport from noisy human spatio-temporal traces. Assess Monte Carlo methods like particle filtering and Bayestian network methods (HMM, long short term memory RNNs) while focusing on a practical and scalable design.
Must-haves:
Advantageous:
During your first three weeks you will learn about the data and algorithms of Teralytics, understand the research context and conduct a thorough state-of-the art review. You will get an understanding of our technology platform and the ways we process large amounts of data while protecting people’s privacy.
Two months after joining the company, you will be in the midst of implementing your own machine learning approach to detect trips and modes of transport. You will already have picked up new skills in data science and software engineering by working closely with experts in the field.
At the end of six months you will have completed and benchmarked your prototype, further expanding the Teralytics capabilities in human mobility analysis. While at it you will have gained valuable experience in spatio-temporal data, stochastic processes, and graphical models.
Teralytics is on a mission to change how the world moves.
Until now, cities and mobility services have been designed based on assumptions of how officials and companies expect people to move. But, they aren’t taking everyone’s journey into consideration. As a result, for many, mobility is limited. Not just physically, but socially and economically. It’s stopping people from reaching their full potential.
Teralytics partners with mobile network operators to solve this problem with the most accurate indicator of people’s movement – their mobile devices. It’s the one thing everyone has with them at all times. And the cell towers receiving their signals don’t discriminate based on device model or apps. Due to its complexity and scale, mobile network data has been nearly impossible to understand or utilize. That’s why we’ve pioneered a way to translate it into actionable insights. For the first time we’ve unlocked truly inclusive data on people’s journeys.
Teralytics is backed by leading investors in the mobility and transport space, including Robert Bosch Venture Capital and Deutsche Bahn Digital Ventures, as well as high-profile VC funds, such as Atomico and Lakestar, and we are continuing to grow across the world. Whether at our headquarters in Zurich or in our offices in New York and Singapore, our people are at the core of our mission to shape the future of mobility.
We offer the chance to be part of an exciting and ambitious start-up that puts its people at the heart of its business. Be part of a diverse, international, cross-disciplinary team of highly motivated, hands-on experts that tackle unique challenges with a positive spirit and lots of fun.
We offer a flexible work schedule, additional company holidays, and a central office location.
Teralytics is an equal opportunity employer and we value diversity. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.