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Director, Data Science

Philadelphia, PA, USA | Comcast

  • Industry:
    Media / Entertainment
  • Position Type:
  • Functions:
    General Management
    IT / Information Technology
  • Experience:
    10-12 years
Job Description:
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Responsible for leveraging internal and external data to provide insights and information which supports a facts-based decision making process. Recognized as an expert in own area within organization. Provides leadership, mentoring, and guidance. May participate in the development of business strategy. Decisions are far-reaching in terms of setting precedents for the business and technology.

Core Responsibilities:

- Provides consultative direction for the data science team and the organization as a whole. Develop long-term objectives and plans on tactical and strategic issues. Determines resources, technology and course of action to achieve results.

- Leads the identification and adoption of latest technology developments to ensure we maintain an infrastructure to extract, manage, and analyze data in a scalable way, and to develop best in class risk mitigations, acquisition engines, profitability algorithms, and new product enhancements

Oversee research, design, simulation and/or prototyping new algorithmic product features according to business need.

- Lead development and implementation of scalable algorithmic solutions for real-time solutions to churn, pricing, conversion, and audience segmentations. Managing challenges associated with investigating and understanding large datasets, and building models based on Big Data solutions.

- Build deep partnerships with business, product management, and technology leaders. Use best practice and knowledge of internal and or external business issues to improve products, services and in solving complex problems.

- Educate other departments on data science methodologies, concepts and algorithmic advancements.

- Lead complex interdepartmental data science programs that designs solutions across one or more technologies.

- Review and evaluate data scientist programs enterprise level to determine appropriate use of algorithm-driven products and solutions.

Education Level: Master's degree

Field of Study: Quantitative fields such as Economics, Statistics, Mathematics, Decision Science, Operational Research, Computer Science or Engineering.


Years of Experience: Generally requires 10+ years related experience.


- PhD preferred

- Proven record of work on significant and unique issues, where analysis of situation or data requires an evaluation of intangibles. Can look at the big picture and handle multiple projects at one time. Has good listening skills and demonstrates flexibility

- Advanced level proficiency with statistical probabilistic modeling techniques such as regression, decision trees, neural networks, support vector machines, supervised/unsupervised clustering techniques, etc.

- Expert theoretical knowledge of statistical modeling techniques and advanced applied skills in developing statistical targeting models using at least 2 of the following tools; SAS, R, KNIME, SPSS, Python, RapidMiner, KXEN, Bayesia, MATLAB, Statistica, Weka etc.

- Expert working within enterprise data warehouse environments platforms (Teradata, Netezza, Oracle, etc.) and working within distributed computing platforms such as Hadoop and associated technologies such as SQL, HQL, MapReduce, Spark (MLib, SQL, R Py), Storm, Yarn, Kafka, Sqoop and Hive

- Proficient in at least 1 scripting and/or programming language such as Scala, Julia, C#, Python, Perl, Java, C++

- Has the ability to innovate and evaluate at a senior level; drives solutions through other teams; thinks strategically across one or more technologies. Has a demonstrated track record of in-depth technical exposure and successful project delivery

- Shares data with team. Makes themselves replaceable. Does not develop silo views. Is able to communicate/collaborate with internal and external contacts/vendors; understands context of work outside function

- Possesses a broad or deep scope of influence; is aware and responsive to higher level business drivers. Able to prioritize across teams and forge relationships with other teams. Drive and champion change; innovatively solve problems; is willing to take risks and contribute innovative ideas

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