Data Scientist II
| Company | Lennox International |
|---|---|
| Location | Richardson, TX, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | Bachelor’s |
| Experience Level | Junior, Mid Level |
Requirements
- Requires a bachelor’s degree in a related field (Business, Computer Science, Information Science, Analytics) or an equivalent combination of education and experience.
- Requires at least 1 year related experience.
- Working knowledge of statistical computer languages, such as R, Python, SQL, and PySpark, to manipulate data and interpret insights from large data sets.
- Good understanding of machine learning techniques such as clustering, decision trees, Random Forest, logistic regression, linear regression, gradient boosted trees, and Naive Bayes classifier.
- Good knowledge of visual analytics, with preferable experience of Qlik, Tableau or PowerBI.
- Good understanding of big data architecture and how distributed computing works.
- Working knowledge of database concepts and ability to handle large complex data sets.
- Ability to communicate results and gain consensus with non-technical audience.
- Some experience with SparkMlib combined with strong programming background.
- Working knowledge about analyzing unstructured information.
Responsibilities
- Organizes, analyzes and extracts meaningful information from large amounts of data to build a learning machine which helps in streamlining business processes.
- Assists on machine learning projects that use and promote data-exploration techniques to discover new or previously unasked questions.
- Builds custom data-fueled models and algorithms, uses machine learning tools and statistical techniques to improve decision making capabilities, produce solutions to problems and improve ROI.
- Builds custom data models and algorithms to complex data sets Works on a broad range of Data Science problems across varied business groups.
- Develops expertise in various businesses and help translate that into increasingly high value added advisory solutions to stakeholders.
- Works with others to develop, refine and scale data management and analytics procedures, systems, workflows, best practices and other issues to contribute to the company’s machine learning practice.
Preferred Qualifications
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No preferred qualifications provided.