End to end Machine learning model development - from data collection to preprocessing, cleaning, modeling, hyperparameter tuning, evaluation, interpretation, and deployment.
Develop and demonstrate proficiency with multiple languages, platforms, algorithms, and verticals.
Ability to learn new algorithms and technologies.
Collaborate with internal/external stakeholders to manage data logistics - including data specifications, transfers, structures, and rules.
Access and extract data from a variety of sources of all sizes.
Provide problem-solving and data analysis, derived from programming experience.
Contributing in the development and deployment of applied, predictive and prescriptive analytics.
Develop self-learning systems that can predict failures and autocorrect based on data.
Requirements:
Have good implementation experience with R, Python, Perl, Ruby, Scala, Apache Spark, Storm, and SAS.