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Job Requirements of Data Scientist, Service Metrics:
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Employment Type:
Full-Time
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Location:
Foster City, CA (Onsite)
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Data Scientist, Service Metrics
In this role, you will be instrumental in developing and refining the metrics that assess the company's driving and service performance. You will also take ownership of managing data and metrics from start to finish, dive into data to develop innovative solutions, and generate actionable insights.
Working with cross-functional teams, your work will be pivotal in improving the metrics framework and operational processes. As a result, your work will accelerate the understanding of the company's service, helping the company to provide a convenient, safe, and delightful experience for its riders.
As a Data Scientist, Service Metrics, you'll:
- Take ownership of the service metrics definition and reporting, and work closely with cross-functional teams to interpret data and provide actionable insights.
- Analyze the service metrics to identify trends, patterns, and areas for improvement.
- Partner with data engineers to enhance data collection and transformation methods when needed.
- Analyze data to describe and diagnose service performance and implement analytical models into production.
- Enable a feedback loop with the estimation metric owners to improve estimation methodologies for upcoming milestones.
Requirements
- Bachelor's degree in Data Science, Statistics, Engineering, Business, or a related discipline
- 6+ years of proven experience in data analysis, metrics management, engineering, consulting, or program management
- Strong fluency with Python and SQL
- Strong analytical skills with the ability to translate complex data into actionable insights
- Experience with data visualization tools (e.g., Tableau, Looker, Power BI, etc.)
- Experience with experimentation methods (e.g., A/B testing)
- Strong written and verbal communication skills
Bonus Qualifications:
- Experience with predictive modeling frameworks and machine learning methods
- Experience with a workflow manager such as Airflow
- Experience with data warehouse platforms (e.g., Redshift, BigQuery, Databricks, etc.)
- Experience with large-scale processing frameworks (e.g., Spark, Hadoop, etc.)
- Experience with large-scale streaming platforms (e.g., Kafka, Kinesis)