We are building a top European data science team focused on urban mobility and are looking for exceptional people to join our team.
You will enjoy an opportunity to use your creativity in algorithm design and apply cutting-edge technologies to develop models, automate and optimize data-driven decisions and enable the entire organization to tap into the value of ML technologies.
We partner with people from strong technology companies and computer science institutes.
Some examples of real life projects we work on :
Using external data, such as street imagery, to detect missing street elements, such as traffic signs or POIs
Your daily adventures will include :
Working with a technical stack consisting of Python, Docker, SageMaker, Airflow, Spark, Redshift
Solving real world problems using gradient boosted trees, deep learning, clustering algorithms, Markov models, and more in order to enhance the quality of the maps we use and provide high quality services to our customers globally
Handling the entire lifecycle from exploratory queries and notebook prototypes to a working machine learning pipeline that automates the detection of missing maps elements.
These detections would be added to the map automatically or reviewed manually in the case of low confidence detections
Understanding high value missing maps elements that impact most our clients experience and prioritising them according to improvements in ETA accuracy, the route optimality and the pick-up experience
Leveraging our in-house model lifecycle platform that allows you to launch new projects in a matter of days
Working in product feature teams together with data analysts, product managers and software engineers
Discussing the problems and technical innovations within the broader data science team that provides a pool of peers and mentors
We are looking for :
Industry experience in data science and machine learning (5+ years recommended)
Awareness of both business and technical aspects of data science
Experience in Python programming, including libraries like Pandas, Numpy, sklearn
Understanding and practical experience with statistical hypothesis testing
Proactive mindset, willingness to take initiative and work with little supervision
Hands-on experience with most used methods for dimensionality reduction, clustering algorithms, rankers, regressors, classifiers, etc (PCA, k-means, DBSCAN, Spectral Clustering, Learning-to-rank approaches, gradient boosted trees, deep neural networks, SVM, linear / logistic regression and others)
Enthusiasm to collaborate with different roles in product, analytics and engineering to identify problems, explore trends and discover growth opportunities
Strong verbal and written communication skills in English
Track record of deploying models to production and measuring the impact
You will get extra credits for :
Product development experience in a technology company
Product development experience in a technology company
Familiarity with any cloud systems (AWS, Azure, Google app engine)
PhD, MSc or BSc in a quantitative field
Experience with mapping related aspects and tools (OSM, OSRM) and, generally, with classic graph algorithms
Why you’ll love it here :
Your daily duties will have a meaningful impact on millions of people all over the world.
You’ll be surrounded by the most friendly, supportive colleagues you can imagine.
As we grow, so will you! Bolt’s fast-paced, challenging environment offers you great opportunities for professional development.
You’ll always be kept informed. Our bi-weekly All Hands meetings bring our global teams together, ensuring we’re all up to speed and moving forward as one.
Switching off is important! At Bolt, we like to work hard and play hard. Enjoy our fun team events, office snacks, free merch and more!
