Data Analyst – Data Science

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.