Data Analyst - Data Science

Job Description

Data Analyst – Data Science/Big Data

Philadelphia, PA 19103

Requirements:

• Degree in the following areas: Statistics, Data Science, Computer Science or relevant science or engineering discipline.
• Bring a combination of mathematical rigor and analytical thinking to create recipes that extract relevant insights from billions of rows of data to meaningfully improve user experience.
• 2+ years working within an enterprise data lake/warehouse environment or big data architecture.
• Understanding of machine learningtechniques andalgorithms, both theoretical underpinnings and craft.
• Applied statistics skillsand understanding of probabilitydistributions, statistical testing, regression, etc.
• Experience with common data science toolkits, such asscikit-learn, matplotlib, R, ggplot, etc. (excellence in at least one of these is highly desirable).
• Great communication skills.
• Proficiency in visualization tools, such as D3.js, Tableau, Looker, Amazon Quicksight, Grafana (excellence in at one or more is highly desirable).
• Proficiency in using query languages such as SQL and Hive.
• Good scripting and programming skills in, Python, R, and Scala (excellence in at least one of these is highly desirable).
• Data-oriented personality.

Preferred Additional Skills:

• Experience with working in Spark
• Experience with data visualization tools, such as D3.js, Tableau, Looker, Amazon Quicksight
• Experience with NoSQL databases, such asMongoDB, Redis/ElasticCache,Cassandra, HBase

Description:

• Building a strong intuitive understanding of the problem domain (Next Generation Access Networks) and identifying testable hypotheses to explain interesting phenomena in this domain.
• Selectingand transformingfeatures and building & optimizing classifiers using machine learning techniques.
• Integrating data from multiple sources including third party sources.
• Data mining using state-of-the-art methods.
• Enhancing data collection procedures to include information that is relevant for building analytic systems.
• Frequent meeting/communication with stakeholders to interpret their needs, plan/organize, and discuss progress and results.
• Developing actionable quantitative models in the areas of effectiveness, ROI, pricing and optimization.
• Doing extensive data exploration and ad-hoc analysis and presenting insight in a clear manner.
• Developing and communicating goals, strategies, tactics, project plans, timelines, and key performance metrics to reach goals.