Data Scientist (Remote)

The Athletic; United States; Remote; USD 110,000 - 150,000; Full time

Job summary

The Athletic is seeking a data scientist to apply machine learning to subscription and content recommendations. Join a remote team collaborating with data, product, growth, and engineering to build models and production pipelines.

You will design models to predict subscriber conversion, optimize recommendations, and guide experiments, with an emphasis on clear storytelling with data. 100% remote opportunity for US/Canada.

Qualifications

• 2–4 years of data science or data analytics experience on web-scale data.
• Proficient Python for training models and production deployment.
• Strong SQL skills for data extraction and warehousing.
• Understanding of subscription media and recommendation engines.
• Solid grasp of statistical principles for predictive modeling.
• Excellent written and verbal communication.

Responsibilities

• Design and implement mathematical models to drive key long-term product goals.
• Lead projects with hands-on analysis and modeling.
• Use quantitative analysis to understand user behavior and provide data-driven recommendations.
• Design and develop data science pipelines in production.
• Guide complex experiments and foster a strong data science culture.
• Collaborate with data, product, growth, and engineering teams.

Skills

Python

SQL

Machine Learning

Data Analysis

Communication

Job description

About UsPowered by one of the largest global newsrooms in sports media, The Athletic brings sports fans the most comprehensive stories about the teams, sports and athletes they love. The Athletic’s newsroom of 500+ full-time staff delivers in-depth coverage of hundreds of professional and college teams across more than 47 North American markets and all 20 football clubs in the English Premier League, as well as many Championship clubs.

About the Role

We are looking for a data scientist who will bring a deep understanding of our sportswriting, our in-game experience, and the way our subscribers use The Athletic to help us make our best-in-class product even better. In this role, you will work closely with our data, product, growth, and engineering teams to develop advanced machine learning models. These models will help us understand our business, optimize and automate our workflows, and have our product make smarter recommendations. As a member of our data science team, your work will have a significant impact at The Athletic.

This role is 100% remote for candidates currently based in the United States or Canada.

Responsibilities

• Design, build, and implement mathematical models to drive key long-term product goals, like training a model that predicts which of our free users are most likely to subscribe, or recommending the right articles to the right users at the right time.
• Lead projects with hands-on analysis and modeling, drawing from multiple analytical methods to choose the right tool and right level of complexity appropriate for the job.
• Use quantitative analysis to deeply understand what our users are saying, and to develop recommendations based on that analysis.
• Design and develop data science pipelines in production.
• Guide complex experiments.
• Cultivate an outstanding data science environment.

Requirements

• 2-4 years of experience as a data scientist or data analyst with a proven ability to apply scientific methods to solve real-world problems on web scale data.
• A strong working knowledge of Python and how to use it to train machine learning models and put them into production.
• A solid understanding of SQL with an ability to efficiently pull and write to our data warehouses.
• An understanding of subscription media and recommendation engines, with the ability to turn it into insights and optimizations.
• Knowledge of statistical principles and how they apply to predictive modeling.
• Strong verbal and written communication skills with ability to build cross-functional relationships.
• Can explain sophisticated concepts to diverse audiences and craft compelling stories with data.