The Data Scientist is responsible for analyzing raw information through statistical analysis to discover trends or patterns that can be used for building data products to extract valuable business insights.
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Actively participates on cross-functional project teams developing analytics enabled solutions
Analyzes raw information through statistical analysis to discover trends or patterns that can be used for building data products to extract valuable business insights
Utilizes machine learning algorithms and models and presents them using data visualization techniques
Generate relevant, actionable insights based on iterative data analysis, translate data-driven output into business contexts, and to make appropriate business recommendations
Has strong familiarity with statistical software packages such as R, Python, SAS and Scala as well as different end-to-end data science and advanced analytics tools such as Microsoft Azure Machine Learning
Has an exceptional understanding of the different software utilities for data and computation such as Hadoop, Apache Spark and Impala
Develops processes and tools to monitor and analyze model performance and data accuracy
Synthesizes data and methodologies into actionable business strategies
Enhances data collection procedures for building analytical systems
Uses predictive modeling to increase and optimize customer experiences, revenue generation and other business outcomes
Leads training and informational sessions on statistics.
Owns their career development. Takes advantage of available resources to advance their technical and business skills.
Understands how their assigned tasks fit into the broader initiative
What you should have
Minimum Bachelor’s degree in Statistics, Math, Computer Science, Engineering; or equivalent work experience, however, an advanced degree in a quantitative discipline would be preferred.
Basic Working knowledge of programming languages and concepts (e.g. SAS, SPSS, R, or Python)
Basic understanding of different techniques and knowledge of machine learning algorithms. Basic knowledge of AI toolkits.
Basic knowledge of data visualization tools such as Tableau, QlikView, open source data visualization libraries - ggplot and d3.js
Basic knowledge of common database querying languages such as SQL. Capability of applying a systematic, structured approach to explore / navigate / mine massive amount of data, structured or un-structured, to uncover hidden patterns / insights. Some working knowledge of relational database models (e.g. data vaulting techniques, rules tables).