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# Data Scientist / Data Engineer

- **Company:** Thunder, Llc
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Big Data, Databases, Computer Engineering, Information Engineering, Data Structures, Software Design Patterns, Distributed Systems, Apache Hadoop, Apache HBase, Apache Hive, Machine Learning, MongoDB, Recommender Systems, SQL Databases, Computational Statistics, Data Streaming, Apache Spark, Information Technology, Druid, Apache Kafka
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/data-scientist-data-engineer-san-francisco-ca-thunder-8379983

## About the Role

Ideal candidates will have hands-on, operational experience building and operating large-scale data analytics services and thrive working in a fast-paced startup environment.

 * 5 -7 years of hands-on experience with using advanced statistics techniques and machine learning to build operational production services
 * Strong understanding of machine learning, recommendation systems, predictive analytics, and multivariate analysis
 * Strong computer science fundamentals including data structures, algorithms, distributed systems and common design patterns
 * Strong database and data engineering experience with hands-on experience building services that leverage a variety of database systems including SQL, Redshift Spectrum, Druid, Hadoop, Hive, HBase, Spark, Kafka, AWS Kinesis, MongoDB
 * B.S. or M.S. in Computer Science, Computer Engineering, Mathematics, Statistics, Applied Mathematics or related experience

## Description

* Design and develop Big Data and real-time analytics solutions using industry standard technologies
 * Collaborate with internal business and product teams to identify product features that can be powered by advanced data analytics
 * Use various machine learning and statistical techniques to analyze data, build models and identify requirements for operationalizing those models into production services
 * Work with external customers on challenging data analysis problems


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