Data Scientist

at  Discovery Education

At Discovery Education, we are dedicated to creating exceptional educational resources that inspire students and teachers. We are looking for a Data Scientist to join our Decision Science team. You will play a crucial role in guiding product development by utilizing advanced statistical modeling, predictive analytics, rigorous measurement (A/B testing), and machine learning techniques. You will become an expert on our users, empowering data-informed decision making. Positioned within the Research and Analytics vertical, you will work cross-functionally with product managers, user researchers, designers, engineers, and data analysts to fuel product growth and enhance educational content and delivery.

About the Role

• Collect and clean large datasets, potentially including a mixture of numeric, categorical, text, sequential, and unstructured data.
• Collaborate closely with product managers to design, analyze, and interpret A/B tests, directly influencing product design and strategy based on robust statistical analysis.
• Develop models to predict user behaviors such as activation, engagement, and retention, translating complex datasets into actionable insights.
• Conduct machine learning (ML) or statistical analyses, and build pragmatic, and statistically rigorous solutions to deliver actionable insights, accurate predictions, and effective optimizations.
• Build data products and analytical frameworks that accelerate and scale insights delivery.
• Work with engineers to log new data sources and track events as we build new features.
• Create and maintain dashboards that visualize key metrics, enabling stakeholders to make informed decisions quickly and efficiently.
• Help establish a culture of data-driven product development by fostering cross-functional partnerships and effectively communicating insights & best practices.
• Maintain responsibility for the documentation of data pipelines, and technical strategies, analyses, and machine learning models to ensure transparency and reproducibility.

Requirements

• Bachelor’s degree (computer science, data science, physics, mathematics, economics, or related field) with 2+ years experience in data science, or Master’s degree with 1+ years experience in industry data science, or a Ph.D.
• Demonstrated track record of real-world data science projects from conception to deployment.
• Programming experience in Python, R, or Java, and experience with statistical and machine learning libraries and frameworks.
• Proficient in data manipulation and analysis tools (e.g., Pandas, NumPy) and familiar with data visualization technologies (e.g., Matplotlib, Seaborn).
• Experience designing and executing rigorous experiments (A/B tests) with clear insights and actionable recommendations.
• Familiarity with usage metrics relating to acquisition, activation, engagement, & retention, and/or have experience with user segmentation, recommendations, and personalization.
• An intuition for applying quantitative models to uncover user pain points and identify patterns.
• Excellent communication skills with the ability to effectively interact with product managers, engineers, and executives.
• Demonstrated problem-solving capabilities, attention to detail, and the ability to think critically on ambiguous problems.
• Familiarity with modern analytics and cloud computing tools (e.g. Heroku, AWS, PostgreSQL, Snowflake, dbt) is a plus.
• Familiarity with product-led growth or SaaS enterprise models is a plus.
• Experience or familiarity with Education or EdTech is a plus but not required.
• Experience with graph databases and network analysis is a plus.
• Experience with product development methodologies such as Agile is a plus.
• Legal right to work the United States.

NOTICE

Discovery Education, Inc. is seeking to employ one H-1B nonimmigrant in the occupational classification of Data Scientist at a salary of $97,968 for the period from 10/01/2024 to 09/30/2027 at the location of 1209 Boylston Street, Apt. 27, Boston, MA 02215. The labor condition application is available for public inspection at the same address. Complaints alleging misrepresentation of material facts in the labor condition application and/or failure to comply with the terms of the labor condition application may be filed with the U.S. Department of Labor, Employment and Training Administration, Chicago National Processing Center, 536 South Clark Street, 12th Floor, Chicago, Illinois 60605–1509, telephone: (312) 886–8000, facsimile: (312) 353–3352, help desk email: plc.chicago@dol.gov, or with any office of the Wage and Hour Division of the United States Department of Labor.
Benefits

We are proud to offer employees and their families a comprehensive benefits package:

• Medical-Dental-Vision
• Health Care Dependent Care
• Short & Long Term Disability
• Summer Hours
• Life Insurance
• 401(k)

• Collect and clean large datasets, potentially including a mixture of numeric, categorical, text, sequential, and unstructured data.
• Collaborate closely with product managers to design, analyze, and interpret A/B tests, directly influencing product design and strategy based on robust statistical analysis.
• Develop models to predict user behaviors such as activation, engagement, and retention, translating complex datasets into actionable insights.
• Conduct machine learning (ML) or statistical analyses, and build pragmatic, and statistically rigorous solutions to deliver actionable insights, accurate predictions, and effective optimizations.
• Build data products and analytical frameworks that accelerate and scale insights delivery.
• Work with engineers to log new data sources and track events as we build new features.
• Create and maintain dashboards that visualize key metrics, enabling stakeholders to make informed decisions quickly and efficiently.
• Help establish a culture of data-driven product development by fostering cross-functional partnerships and effectively communicating insights & best practices.
• Maintain responsibility for the documentation of data pipelines, and technical strategies, analyses, and machine learning models to ensure transparency and reproducibility