The Data Scientist will work on a variety of data-driven initiatives or projects that will result in the development and deployment of data-driven solutions to business problems, strategy, and opportunities.
Requirements
- 5 or more years machine and/or deep learning, data mining, large datasets, and complex relational data models (structured and unstructured).
- 5 or more years programming with Python.
- 3 or more years experience with TensorFlow, Scikit-learn, Keras, Torch and NLP libraries.
- 2 or more years Cloud computing experience.
- Working knowledge of various supervised and unsupervised learning methods such as logistic regression, bagging & boosting, clustering, neural nets, etc.
- Working knowledge of machine learning/deep learning techniques, applications, and libraries.
- Hands-on experience with AWS, GCP, or Azure. Able to utilize API endpoints.
Responsibilities
- Explore data and the implementation of varied data-driven methods to impact enterprise-level strategy, facilitate decision making, and achieve key objectives.
- Apply advanced analytics and machine learning during implementation, as needed based on project scope.
- Extract and transform data from multiple sources in preparation for prescriptive or predictive data analysis, modeling, and reporting.
- Deploy tools to integrate, report, and interpret large data sets with structured and unstructured data.
- Apply supervised and unsupervised learning methods to glean meaningful information from various data.
- Utilize publicly available machine learning or deep learning tools such as TensorFlow, Keras, Scikit-learn, etc. in pursuit of business objectives
- May mentor and support other data-related roles to provide data-driven tools and insights
Other
- Hybrid schedule will include 2 days/week in the office.
- Core working hours Monday-Friday 8am-5pm.
- Bachelor's Degree or the equivalent experience.
- Master's Degree or the equivalent experience. (Preferred)
- Effective communications skills and ability to articulate recommendations to varied audiences
