Develop a deep understanding of business data-sets through a combination of database queries, and exploratory statistical analysis and visualization
Formulate business needs as data science problems where applicable and effectively communicate them to peers, managers and key stakeholders, including the benefits and limitations of models.
Design and develop Machine Learning models and algorithms that drive performance and provide insights, from prototyping to production deployment, across key areas of interest to the company
Partner closely with Infrastructure and Application teams to help develop architecture and implementation of modeling efforts to ensure performance and scalability
Experience working with data sets; processing, analyzing, and communicating results.
Ability to clearly explain findings from complex analyses in case studies and through visualizations
A passion for empirical research and for answering hard questions with data in a clear and precise way
Ability to take high level, loosely-defined business problems and identify precise, quantitative solutions
Familiarity with relational databases and SQL, data transformation (ETL), data mining, ad-hoc analysis
Experience conducting methods using any of the following: machine learning, predictive modeling, statistical inference, experimental design, data mining, and optimization
Solid understanding of a broad range of Regression techniques
Experience coding with at least two of the following: R, Python (including scientific libraries SciPy, Scikit, Pandas, and NumPy preferred), Scala, and SQL languages for interactions with relational databases
[Desired] Experience in Linux/Unix environment and shell scripting
[Desired] Familiarity with Big Data platforms such as Hadoop, Spark, building data warehouses and data lakes in Amazon Web Services and/or Microsoft Azure
Ability to communicate results and progress internally and externally in meetings, presentations, and tech talks
Minimum of a Bachelor’s Degree in a quantitative field (computer science, mathematics, statistics, physics, engineering, etc.); research experience or relevant graduate studies a plus.