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Principal Data Scientist
12 month+ contract
Los Angeles: Onsite Monday-Thursday
Must haves:
10 years in Data Science
General computing (Python, R, Scala)
Solid understanding of Machine Learning Technologies
Basic Qualifications
- MS/Ph.D. in quantitative discipline
- 10+ years of experience in data science, with a proven track record of leading and delivering successful projects
- Proficiency in programming languages such as Python, R, or Scala
- Expert-level experience working with scikit-learn, numpy, and pandas and other ML libraries.
- Solid understanding of ML technologies, models, mathematics and statistical methods
- Experience with cloud databases, such as Snowflake, databricks, and BigQuery is a plus
Required Education College Degree in related fields listed
Preferred Education Master’s or Ph.D. degree preferred in Statistics
Additional Information
Seeking a highly skilled and innovative full-Stack Principal Data Scientist to lead our data science initiatives and drive impactful insights. As a Principal Data Scientist, you will play a crucial role in leveraging data to enhance decision-making, optimize processes, and uncover opportunities for growth. You will collaborate with cross-functional teams to develop and implement data-driven strategies that align with our mission.
To be successful in this role, you should have a strong background in Machine Learning and experience with AI technologies. Our ideal candidate will have a passion for developing cutting-edge technology and the ability to think creatively to solve complex problems
- Develop novel AI/ML solutions to further the company’s mission of creating innovative and engaging content
- Architect efficient data models and high-quality production pipelines and partner with Engineering for all necessary telemetry
- Design, build, and deploy predictive models, machine learning algorithms, and statistical analyses to address complex business challenges.
- Identify trends, patterns, and insights from large datasets to inform decision-making.
- Work end to end from data collection, feature generation and selection, algorithm development, forecasting, visualization and communicating of model results
- Think creatively to solve complex problems
- Monitor and maintain the performance of ML models
- Research and stay up-to-date on the latest AI/ML trends and technologies
- Provide technical guidance and mentorship to other engineers
- Collaborate with data engineering teams to ensure the quality and accessibility of data
- Communicate insights and best practices in leadership
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