Data Scientist II

About

Lennox (NYSE: LII) Driven by 130 years of legacy, HVAC and refrigeration success, Lennox provides our residential and commercial customers with industry-leading climate-control solutions. At Lennox, we win as a team, aiming for excellence and delivering innovative, sustainable products and services. Our culture guides us and creates a workplace where all employees feel heard and welcomed. Lennox is a global community that values each team member’s contributions and offers a supportive environment for career development. Come, stay, and grow with us.

General Duties

• 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.

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