Data Scientist II

Company Overview: Lennox International (LII) is a leading global provider of innovative climate control solutions for heating, ventilation, air conditioning, and refrigeration (HVACR) markets.

Beginning over a century ago, Lennox International has built a strong heritage of Innovation and Responsibility. Our position as an innovation leader continually inspires us to promote more efficient energy use and a healthier environment through our product operations. Our engaged and diverse workforce is committed to providing climate control solutions that provide the most value and comfort for our customers.

We are proud to have instilled a shared sense of responsibility and commitment among our approximately 10,000 employees located throughout North America, South America, Europe, Asia, and Australia. Job Description:

• 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.

Qualifications: 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.

Excellent problem solving skills, ability to apply quantitative and qualitative methods. Good Knowledge of statistical computer languages (R, Python, SQL, PySpark, etc.) to manipulate data and interpret insights from large data sets. Understanding of machine learning techniques (clustering, decision trees, Random Forest, Logistic regression, Linear regression, Gradient boosted trees, naïve bayes classifier). Understanding of big data architecture and how distributed computing works. Understanding of basic database concepts, ability to handle large complex data sets. Ability to communicate results and gain consensus with non-technical audience. Experience with SparkMlib combined with strong programming background. Basic knowledge about analyzing unstructured information. Strong knowledge about Visual analytics, preferable experience with Qlik, Tableau or PowerBI.