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Data Scientist Supervisor
2 months ago
Location: Los Angeles, CA 90012 (Hybrid)
Duration: 06+ Months Contract with potential for extension
Skills Required:
The Data Science Supervisor has experience working directly with a client-centric relational database and managing individuals
They have strong technical knowledge and are comfortable advising on complex buildouts within an SQL database
They are comfortable with various analytical tools and have taken the lead on projects where the best path forward is unclear
They are seasoned and effective facilitators with a customer service mindset
The Data Science Supervisor deeply understands machine learning techniques, including supervised and unsupervised learning, such as regression, classification, clustering, and dimensionality reduction
Proficiency in statistical analysis and hypothesis testing is essential for interpreting data and validating models
Expertise in data manipulation using tools like Pandas, NumPy, and Scikit-learn, coupled with familiarity with big data technologies such as Apache, Hadoop, and Spark, is crucial for handling and transforming large datasets
Advanced skills in data visualization with Matplotlib, Seaborn, Tableau, or Power BI are required to create insightful visualizations
Proficiency in Python and R for data analysis and SQL for database querying is expected, alongside experience in deploying machine learning models using tools like Docker, Flask, or cloud solutions (AWS, Azure)
Additionally, knowledge of data engineering practices, including data pipelines, ETL processes, and data warehousing, is essential for efficient data management and processing.
Experience Required:
Six (6) years of experience, including three (3) years supervising a team of data science professionals serving as subject matter experts and coordinating and overseeing complex data science projects to support program, policy, and operational decision-making.
Education Required:
This classification requires possession of a bachelor’s degree or higher
A Master’s or Doctoral degree from an accredited college or university in applied research, such as Data Science, Machine Learning, Mathematics, Statistics, Business Analytics, Psychology, or Public Health, may be substituted for two (2) years toward the minimum years of qualifying experience.
