Head of Data Science
- Data Science
- San Francisco
- Manage and mentor a team of data scientists and data engineers, and work closely with the rest of the product development organization.
- Set the technical direction on the Data Science team for a company with hundreds of millions of users, and big ambitions.
- Own the architecture, delivery, and evolution of interrelated big data systems.
- Follow good engineering practices, such as architectural design, unit testing, test driven development.
- Make technical decisions and practice a high level of ownership in a multi-datacenter, resilient service-oriented architecture with autoscaling.
- Build and optimize a data and computational infrastructure that can simultaneously handle batch large scale analytics, real time streaming analytics and perform machine learning, training and prediction to serve hundreds of millions of users.
- Work with the DevOps team to ensure that all the required monitoring, exception handling and fault tolerance is in place to maximize robustness of the data architecture.
- Build fault tolerant distributed machine learning workflows, starting from R&D all the way to production.
- Develop and maintain flexible event tracking and querying pipeline for experiment analysis and analytics.
- Work closely with our Business Intelligence and Analytics team to ensure robust and scalable data warehousing to support their needs.
- Serve as a data science liason to the rest of the product organization, both to help provide insight into existing priorities and to surface opportunities to build data science driven products.
- Design innovative A/B testing techniques to enhance discovery and personalization.
- Research and implement natural language processing algorithms to aid in understanding and classification in our unique petition data.
And here are the skills & experience we hope you have:
- 3+ years of experience with column oriented databases (e.g. Redshift, Snowflake, Vertica) and optimizing dimensional warehouse data models.
- 5+ years industry experience writing and optimizing queries in languages such as SQL and CQL.
- 3+ years industry experience in working independently within a cross-functional engineering team.
- 2+ years of experience in developing a data pipeline with custom ETL that accommodates batch and streaming analytics.
- 2+ years of experience in using distributed computing architectures such as AWS products (e.g. EC2, Redshift, EMR), Hadoop, Spark and effective use of map-reduce, SQL and Cassandra to solve big data type problems.
- Experience in developing production software in languages such as Ruby, Java, Python.
- Experience with modeling and analysis, statistics, machine learning, and/or large-scale data mining.
- Deep experience in the data science methodology from exploratory data analysis, feature engineering, model selection, deployment of the model at scale and model evaluation by using AB testing in production.
- The ability to explain deeply technical concepts, algorithms and products to colleagues of various technical levels is a must have.
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