Data Science/MLE Tech Lead

Ness Ziona, Israel
R&D
Full-time
About The Position

About the job

Moovit (www.moovit.com), part of Mobileye (Nasdaq: MBLY), is the creator behind the #1 urban mobility app and is a leading Mobility as a Service (MaaS) solutions provider. Together, Moovit and Mobileye are accelerating the global adoption of autonomous transportation.

Moovit’s iOS, Android, and Web apps guide people in getting around town effectively and conveniently, using any mode of transport. Introduced in 2012, it now serves over 1.5 Billion users in more than 3,500 cities across 112 countries.

Moovit amasses up to six billion anonymous data points a day to add to the world’s largest repository of transit and urban mobility data. For governments, cities, transit agencies, and private companies, Moovit offers AI-powered MaaS solutions covering planning, operations, and optimization with proven value in reducing congestion, growing ridership, and increasing efficiency and asset utilization.

We are looking for a Data Science / Machine Learning Engineer Tech Lead to join the data science group. You’ll have the opportunity to make a significant impact by creating products that are used by millions of users around the world.

Moovit’s Data Science group is responsible for developing and implementing data-driven models, conducting research to evaluate new data science methodologies, and analyzing large datasets to extract actionable insights.

The individual will collaborate with cross-functional teams to ensure actual impact on Moovit’s users and business outcomes.

Responsibilities:

Software Engineering: Design, develop and implement data pipelines infrastructure, work closely with group members and peers
Machine Learning: Design, Research, analyze, develop, and implement predictive models, in addition to MLOps infra for various of machine learning techniques to address complex business problems.
Data Science Group focal point for aspects of code review, code quality, unit testing, E2E testing, ML models monitoring, maintainability and scalability
Collaboration: Work closely with cross-functional teams including Devops, Back End, and Mobile teams to define project goals and deliver data-science projects into production.
Stay updated with the latest research and advancements in MLOps, machine learning and AI.
Reporting: Create clear, concise, and actionable reports to communicate insights to stakeholders.
Requirements
5+ years of professional experience in Backend/ML Engineering in a tech company that involves working with researchers.
5+ years of professional experience in Data Science - research, data analysis and implementation of predictive models
Experience with Machine Learning: Proven track record in productization of ML models
Proficiency in Python: Strong coding skills with a focus on writing efficient and scalable ML code
Production Experience: Demonstrated ability to deploy ML models in production, ensuring reliability, scalability, and real-time processing
Technologies: In particular AWS, Airflow and Software container technology - Docker, Kubernetes
B.Sc/M.Sc in a scientific field - Computer Science, Software Engineering, Data Science
Initiator and Promoter: Self-starter who actively proposes new ideas and promotes innovative solutions
Learner: Passion for continuous learning, staying updated on the latest SW and ML trends, tools, and research
Flexible Mindset: Adaptability in approaching problems and adjusting models or strategies as needed