Data Scientist II — ML, Pipelines & Impact

Garmin Ltd.; Boulder (CO); On-site; USD 105,000 - 145,000; Full time

Job summary

Garmin Ltd. in the Boulder/Louisville area is seeking a full-time Data Scientist 2 to analyze complex datasets, develop machine learning models, and collaborate with cross-functional teams to deliver AI solutions that inform product strategy.

You will design features, build scalable models, and help translate data-driven insights into actionable recommendations for the business. The role includes creating and maintaining data pipelines, performing EDA, deploying models, and ensuring data quality

Qualifications

• Bachelor's degree and at least 1 year of relevant experience
• Experience with SQL, Python, or R
• Knowledge of ML frameworks (Scikit-Learn, TensorFlow, PyTorch)
• Strong data analysis and statistics knowledge
• Effective communication and teamwork

Responsibilities

• Lead collection, cleaning, and preprocessing of large datasets
• Develop predictive models using ML and statistics
• Create data pipelines for annotation, training and evaluation
• Collaborate with cross-functional teams to translate insights into product strategy
• Monitor model performance post-deployment and iterate
• Create data visualizations and reports to communicate findings
• Identify external/internal data sources to inform models

Skills

SQL

Python

R

Machine Learning

Data Visualization

Communication

Teamwork

Problem Solving

Education

Bachelor's degree in CS/EE/CE/SE/Engineering

Tools

Scikit-Learn

TensorFlow

PyTorch

Job description

Garmin Ltd. in the Boulder/Louisville area is seeking a full-time Data Scientist 2 to analyze complex datasets, develop machine learning models, and collaborate with cross-functional teams to deliver AI solutions that inform product strategy.

You will design features, build scalable models, and help translate data-driven insights into actionable recommendations for the business. The role includes creating and maintaining data pipelines, performing EDA, deploying models, and ensuring data quality