Job Description

• Process, cleanse, and verify the integrity, accuracy, and consistency of data used for analysis.
• Perform ad‑hoc analyses and present insights and findings in a clear and structured manner to stakeholders.
• Analyze large and complex datasets to extract insights and determine the most appropriate analytical or machine‑learning techniques.
• Select features, build, train, evaluate, and optimize machine‑learning models and classifiers.
• Apply data mining techniques using state‑of‑the‑art methods to identify patterns, trends, and anomalies.
• Use data modeling and evaluation strategies to predict future trends and support proactive decision‑making.
• Develop and enhance data collection procedures to improve the relevance and quality of data used in analytical systems.
• Enrich internal datasets using third‑party data sources where required.
• Design and develop dashboards and reports to effectively communicate insights and KPIs to business stakeholders.
• Collaborate with business partners to understand requirements, translate them into data‑driven solutions, and communicate recommendations effectively.
• Work in an Agile environment for applied research and analytics delivery.
• Read and adhere to the Underwriters Laboratories Code of Conduct and follow all physical and digital security practices.
• Perform other duties as directed.

• Bachelor’s degree in Data Science, Data Analytics, Computer Science, or a related field of science.
• Minimum 4+ years of hands‑on experience in data science, data analytics, or a related analytical role.
• Strong problem‑solving skills with an emphasis on analytical and business impact.
• Strong proficiency in SQL, including complex joins, aggregations, subqueries, and performance optimization.
• Experience querying databases and working with large datasets.
• Strong Python programming skills for data analysis, modeling, and statistical computation.
• Experience using statistical and data manipulation libraries such as Pandas and NumPy.
• Hands‑on experience with a variety of machine‑learning techniques, including understanding their real‑world advantages and limitations.
• Detailed knowledge of machine‑learning evaluation metrics and best practices.
• Experience with end‑to‑end machine‑learning development, from data preparation through model evaluation.
• Experience with data visualization tools, preferably Power BI, including calculated columns and business logic (DAX).
• Experience validating analytical outputs and dashboard metrics against source data.
• Strong analytical, communication, and presentation skills.
• Demonstrated effective interpersonal, influencing, collaboration, and listening skills.