Data Analyst - Data Science Job at Cloud Analytics Technologies LLC, Philadelphia, PA

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  • Cloud Analytics Technologies LLC
  • Philadelphia, PA

Job Description

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.

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