Scientist 3, Data Science - 7108

Posted:
10/3/2024, 2:15:59 AM

Location(s):
Philadelphia, Pennsylvania, United States ⋅ Pennsylvania, United States

Experience Level(s):
Mid Level ⋅ Senior

Field(s):
AI & Machine Learning ⋅ Data & Analytics

Workplace Type:
Hybrid

Make your mark at Comcast -- a Fortune 30 global media and technology company. From the connectivity and platforms we provide, to the content and experiences we create, we reach hundreds of millions of customers, viewers, and guests worldwide. Become part of our award-winning technology team that turns big ideas into cutting-edge products, platforms, and solutions that our customers love. We create space to innovate, and we recognize, reward, and invest in your ideas, while ensuring you can proudly bring your authentic self to the workplace. Join us. You’ll do the best work of your career right here at Comcast. (In most cases, Comcast prefers to have employees on-site collaborating unless the team has been designated as virtual due to the nature of their work. If a position is listed with both office locations and virtual offerings, Comcast may be willing to consider candidates who live greater than 100 miles from the office for the remote option.)

Job Summary

Job Description

DUTIES:                            Contribute to a team responsible for analyzing large amounts of data to understand how the company can improve its products; perform statistical analysis on Media and Entertainment industry data; design and run experimental large-scale A/B tests; develop, scale and deploy machine learning models into production environment; work with Big Data tools including Spark, Hadoop, Hive, and AWS Cloud; use Python, Java and Scala; use machine learning techniques and advanced analytics techniques including Regression algorithms, Clustering, Natural Language Processing, Ensemble Modeling, Time Series Analysis, Simulation, and Optimization; design and develop LookML models, explores, dashboards, and workflows in Looker platform; work within the entire data science project life cycle and its phases, including data acquisition, data cleaning, data engineering, features scaling, features engineering, statistical modeling, testing and validation, and data visualization; develop and execute statistical and mathematical solutions to business problems; frame problems, develop roadmaps, and communicate the intended approach and quantitative methods to develop solutions; build customer centric models and optimization tools to support large scale projects that utilize online and offline data, structure and unstructured data, set top box data, and media, behavioral, and attitudinal data; develop and deploy predictive models based on historical data that provide future predictions about customer behavior; build and improve internal data analysis tools; construct forecasts, recommendations, and strategic and tactical plans based on applying data science techniques to business data; leverage internal and external data to provide insights and information that support a facts-based decision making process; and provide input into strategy, data analysis methods, and tool selections. Position is eligible for 100% remote work.

REQUIREMENTS:            Master’s degree, or foreign equivalent, in Data Science, Analytics, Finance, Statistics, Mathematics, Computer Science, or any related quantitative field, and one (1) year of experience performing statistical analysis on data; designing and running experimental large-scale A/B tests; developing, scaling and deploying machine learning models into production environment; working with Big Data tools including Spark, Hadoop, Hive, and AWS Cloud; using Python, Java, and Scala; using machine learning techniques and advanced analytics techniques including regression algorithms, Clustering, Natural Language Processing, Ensemble Modeling, Time Series Analysis, Simulation, and Optimization; designing and developing LookML models, explores, dashboards, and workflows in the Looker platform; and working within the entire data science project life cycle and its phases, including data acquisition, data cleaning, data engineering, features scaling, features engineering, statistical modeling, testing and validation, and data visualization.


Disclaimer:This information has been designed to indicate the general nature and level of work performed by employees in this role. It is not designed to contain or be interpreted as a comprehensive inventory of all duties, responsibilities and qualifications.
 

Skills

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