Job Description

Responsibilities

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.

Qualifications

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.