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Job Summary： Trane Technologies Modeling and Simulation NoE is seeking a Data Analytics Engineer M&S to join a world-class team applying innovative analytic approaches to extremely complex problems in the areas of industrial internet (IoT), equipment and building performance management and related engineering domains.   The Data Analytics Engineer M&S supports the design and development of data-based models applicable to TT products, maintains them for quality, as well as improves and standardizes application of tools and applications across different business units, providing sound guidance to end-users and sharing best practices globally. This position is focused on developing models to improve Trane technologies products, building energy modeling tools including modeling techniques, data richness and quality.  The incumbent is also responsible for model management, integrating with existing systems, as well as identifying and implementing improvements to these processes. 

Hybrid Role expectations:  Face -to- Face availability for 2-3 days/month at the base location (Bloomington, MN). The visits will be governed by timings of  a) face to face technical/business meetings with SBU teams, b) Monthly project update meetings, c) Lab visits as required by the projects.

Responsibilities: 

• Support development and implementation of state of the art statistical and machine learning engineering models to enable predictive and prescriptive strategies
• Develop data pipelines to access field level devices and cloud based storage.
• Develop performance models using data engineering and machine learning for any instrumented piece of equipment, including specific components/subsystems (compressors, heat exchangers, pumps, coils and/or fans).
• Test and evaluate the quality of algorithms using statistical methods.
• Interface with other engineers and modelers to create and improve understanding of predictive and descriptive models.
• Perform model verification and validation and establish standard processes and tools supporting it.
• Improve and standardize application of tools and models across business units, providing sound guidance and training to end-users and sharing best practices.
• Develop a clear understanding of customer requirements and meet or exceed those expectations.
• Conceptualize & develop project ideas leading to new advanced capability development in related domain areas to keep TT to the leading edge of technology.
• Identify internal and external collaborative opportunities in the domain area.
• Lead internal projects and reports project progress for quality, deliverables and cost.
• Work in collaboration with control and system engineers to utilize the dynamic system models created for control algorithm development, applying them to reliability engineering and fault detection.
• Network across the organization to identify and communicate opportunities to apply data engineering and machine learning to product design and processes
• Collaborate with internal customers to address design and maintenance challenges through modeling and simulation.
• Travel – 5-10%

Qualifications:

• Bachelor’s or Master's Degree in Computer Science, Data Science (or Statistics-related discipline), or Engineering.
• At least 2 years of experience applying data-driven modeling

Key Competencies:

• Intermediate knowledge in the following technologies: R, Python, SQL and ability to adapt to new technologies, as necessary.  Familiar with tools such as Alteryx, SAS, Tableau, RapidMiner is a plus.
• Understanding of engineering data and physics-based models and simulations.
• Intermediate knowledge of machine learning modeling techniques, and experience visualizing model outputs for engineers, customers and stakeholders.
• Communicate compellingly.  Convert insights from analytics to stories.  Be an advocate for data science and machine learning, and how to create new capabilities and drive transformation. 
• Experience in statistical inference, unsupervised machine learning, supervised machine learning, reliability/survivability models, and/or predictive maintenance.  Basic experience with neural nets, cross validation, hyperparameter tuning is desired.
• Good understanding of large-scale data mining and machine learning techniques for clustering, classification, regression, and anomaly detection.
• Ability to manipulate and visualize both structured and non-structured datasets.
• Advanced knowledge of database systems including relational, column store and data marts
• Strong written & verbal English communication skills to interface effectively with team members, customers and stakeholders (senior leaders) in North America and other parts of the world.

We offer competitive compensation and comprehensive benefits and programs. We are an equal opportunity employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, pregnancy, age, marital status, disability, status as a protected veteran, or any legally protected status.