The Personalized Experience team aims to give Waze drivers personalized, hyper-local, contextual and timely routing experience. The team contributes to the Personalized Navigation squad and more. The team members are embedded within the Waze development Squads. We develop and own Machine-Learning models from ideation to production, including research, production grade pipelines, feature engineering, experimentation, monitoring and dashboards, inspection and debugging.
The team works closely with the product team, helping to shape the requirements and success metrics. The team works closely with the Backend teams, integrating the models, ensuring a feedback loop to enrich the models, triage and solve users complaints, leading and guiding experimentation and more.
Waze is where people and technology meet to solve transportation challenges. It's a platform that empowers users to contribute road data and edit Waze maps to improve the way we move about the world. As the social navigation pioneer, Waze leverages mobile technology and a passionate global community to redefine expectations of today’s maps.
Responsibilities
- Feature engineering, model evaluation and error analysis, data processing and pipelines, model tuning, monitoring and dashboards.
- Work with product managers, software developers and quality analysts to define requirements, formulate success metrics and lead experimentation.
- Use data to help products to shape the future of Waze and drive innovation. Provide insights on user behavior and help engineers and quality analysts detect and solve complex problems.
- Utilize Google Cloud Platform (GCP), Vertex AI, Python, Scikit-learn, TensorFlow, Airflow/Composer, Python and Looker tools.
Qualifications
Minimum qualifications:
- Master's degree in Statistics, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
- 2 years of experience using data engineering and machine learning to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis or a PhD degree.
Preferred qualifications:
- 3 years as an applied ML engineer applying advanced deep learning models and classic machine learning to solve real-world problems in scale.
- Experience with relevant libraries and frameworks (e.g., TensorFlow, Scikit-learn, PyTorch, etc.).
- Experience in building and managing large-scale data processing pipelines to generate features and feed advanced dashboards and rigorous analyses.
- Experience in Cloud Computing and ML Operations.
- Ability to think critically and creatively to navigate complex technical hurdles.
- Ability to convey complex information clearly and concisely, both verbally and in writing, with a focus on providing key implications and actionable insights and recommendations.