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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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