As a Software Engr II here at Honeywell, you will be responsible for the design, development, and maintenance of software applications and systems. You will collaborate with cross-functional teams to define system requirements and ensure the efficient, timely completion of software projects. Your role will involve coding, testing, debugging, and documenting software solutions to meet the needs of our customers and stakeholders.
In this role, you will impact the development of innovative software solutions that drive efficiency and productivity across various industries. You will play a pivotal role in shaping the future of technology and contributing to the success of our projects.
At Honeywell, our people leaders play a critical role in developing and supporting our employees to help them perform at their best and drive change across the company. Help to build a strong, diverse team by recruiting talent, identifying, and developing successors, driving retention and engagement, and fostering an inclusive culture.
ResponsibilitiesKey Responsibilities
- Collect, clean, and preprocess structured and semi‑structured data
- Perform exploratory data analysis (EDA) to identify trends, patterns, and anomalies
- Assist in building and validating basic machine learning models (regression, classification, clustering)
- Support feature engineering and data preparation for ML use cases
- Generate reports, dashboards, and visualizations to communicate insights
- Run model performance evaluations and assist in interpreting results
- Work with senior analysts and data scientists to support AI/ML initiatives
- Document datasets, model assumptions, and analysis outcomes
- Handle ad‑hoc analysis requests from business or technical teams
Required Qualifications
- Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or related field
- 0–2 years of experience (internships, academic projects, or entry‑level roles acceptable)
- Strong understanding of data analysis fundamentals and statistics
- Basic knowledge of machine learning concepts (supervised & unsupervised learning)
- Proficiency in SQL for data querying
- Proficiency in Python for data analysis (Pandas, NumPy, Matplotlib/Seaborn)
- Strong Excel skills (formulas, pivot tables, data analysis)
- Good problem‑solving and communication skills
Preferred Qualifications
- Familiarity with ML libraries
- Exposure to data visualization tools (Power BI, Tableau, or similar)
- Basic understanding of model evaluation metrics (accuracy, precision, recall, RMSE)
- Experience with Jupyter Notebooks or similar environments
- Awareness of data preprocessing techniques (handling missing values, normalization, encoding)
- Basic exposure to cloud platforms or AI tools (Azure, AWS, GCP – optional)
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