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Pursuing a Masters degree in Computer Science, Software Engineering or related E
Experience with any or multiple of the following: Python, Java, Tableau, Jupyter Notebooks, Teradata, Hadoop/Hive, Oracle, JavaScript, SQL, Airflow, Linux, Perl, PHP
Excellent understanding of computer science fundamentals, data structures, and algorithms
Demonstrated experience, familiarity and ease with handling large data sets and crunching numbers
Information Retrieval (search/recommendation/classification) experience or Human Judgment/User Interface experience
Strong written and verbal communication skills with the ability to translate complex problems into simpler terms, and effectively influence both peers and senior leadership
0+ years’ experience in machine learning, advanced predictive modeling, and complex data analysis
Ability to simplify and turn complex business problems into mathematical models
Ability to create low & hi-fidelity usable prototypes
Experience with analysis tools & ability to gain proficiency with new tools quickly, such as SQL, RStudio, PyCharm, Jupyter, Anaconda, VSCode, etc.
Familiar with (or able to learn quickly on the job) Amazon Web Services, such as S3, EC2, Lambda, API Gateway, Elastic BeanStalk, SageMaker.
Excellent relationship management, verbal & written communication skills
A strong passion for empirical research and for answering hard questions with data.
Ability to communicate complex quantitative analysis in a clear, precise, and actionable manner.
Currently enrolled in a degree in quantitative field (e.g. Computer Science, Engineering, Mathematics, Statistics, Operations Research, Economics, or other related fields).
Experience doing quantitative analysis including experience with SQL, other programming languages (e.g, Python) or statistical/mathematical software (e.g, R, SAS, MATLAB).
Experience with statistical methods such as forecasting, time series analysis, experimentation, hypothesis testing, classification, clustering, anomaly detection, regression analysis and/or others.
Experience developing data pipelines, building dashboard, and deploying machine learning models.
Ability to deal with ambiguity, drive projects / analyses to conclusion with limited supervision and communicate analysis in a clear, concise and actionable manner.
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