Sr. Data Scientist

Key Skills

Java Programming, Oracle, Python Programming, C++, SQL, Apache Kafka, Scala Programming, MapReduce, Teradata, GraphQL, YARN, R Programming, Sqoop, SPARK Programming, Storm, C# Programming, Netezza

Job Description

Responsible for leveraging internal and external data to provide insights and information which supports a facts-based decision making process. Provides input into strategy, analysis methods, and tool selection. Acts as a key contributor in a complex and crucial environment. May lead teams or projects and shares expertise

Job Description

Core Responsibilities:

• Develop and deploy predictive models based on historical data that provide future predictions about customer behavior
• Develop data mining, machine learning, statistical and graph-based algorithms designed to analyze massive data sets for business insights and partner with the data engineering team to ensure proper implementation and usage of algorithms
• Mentor and train less experienced team members on analytical projects or on cross-functional teams. Frequently serves as team lead on multiple projects, mentor and train junior team members.
• Review and approve methodologies used for advanced analysis projects (predictive models, clustering/segmentation, etc) by junior team members and others
• Lead complex projects using wide breadth of data sciences and advanced techniques.
• Manages the review, revision and maintenance of existing internal procedures to ensure quality and efficiency. 
• Determine appropriate methods, prove viability of selected method and educate internal teams as to the analytical foundation.
• Use analytical rigor and statistical methods to analyze large amounts of data, extracting actionable insights using advanced statistical techniques such as data analysis, data mining, optimization tools, and machine learning techniques and statistics (e. g., predictive models, LTV, propensity models).
• Lead and collaborate on large scale projects that utilize online & offline data, structured & unstructured data, set top box data (media/behavioral/attitudinal) to build customer centric models and optimization tools.

Education Level: Master's degree preferred

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

Years of Experience: Generally requires 4-5 years related experience if no Advance Degree

Skills:

• PhD or MS in a quantitative field preferred
• Beginner to Inermediate level proficiency with statistical probabilistic modeling techniques such as regression, tree-based methods (Random Forest, GBM), neural networks, support vector machines, supervised/unsupervised clustering techniques (k-means, DBSCAN, Expectation Maximization), principal component and factor analysis, 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, Storm, Yarn, Kafka, Sqoop and Hive
• Expert in at least one programming language such as Python, R, Scala, Julia, C#, Java, C++
• Experience in Natural Language Processing (word categorization, topic modeling, application of machine learning to NLP) a plus
• Ability to explain complex statistical problems and solutions to laymen.
• Has a good understanding of overall business, including financial acumen, ability to convert complex data into insights and action plans, demonstrated in-depth understanding of predictive modeling life cycle and architects projects through implementation

Compliance: Comcast is an EEO/AA/Drug Free Workplace.

Disclaimer: The above 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.

Education

Bachelor's Degree

Relevant Work Experience

2-5 Years

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