Data Scientist II: ML Modeling & Strategic Insights

Garmin; Olathe (KS); On-site; USD 110,000 - 140,000; Full time

Benefits offered by this job

Garmin Benefit Program

Job summary

Garmin seeks a full-time Data Scientist 2 at Garmin's U.S. headquarters in the Greater Kansas City area. You will analyze complex datasets, develop machine learning models, and collaborate with cross-functional teams to provide actionable insights and AI solutions.

Under guidance or with minimal supervision, apply ML, statistics, and data engineering to address challenging business problems and drive data-driven decisions across product strategy and user experiences.

Qualifications

• BS degree in a technical field and 1 year of relevant data science experience.
• Strong foundations in descriptive and inferential statistics.
• Experience with SQL, Python or R for data analysis and modeling.
• Knowledge of ML frameworks such as Scikit-Learn, TensorFlow, or PyTorch.
• Ability to communicate results clearly and work with cross-functional teams.

Responsibilities

• Lead collection, cleaning, and preprocessing of large datasets.
• Conduct advanced EDA to identify trends and anomalies.
• Design, implement, and refine predictive models using ML and statistics.
• Collaborate with cross-functional teams to translate insights into product recommendations.
• Design and deploy data pipelines for annotation, training, evaluation, and automate workflows.
• Monitor model performance post-deployment and ensure data quality and governance.

Skills

Descriptive statistics

Inferential statistics

Data analysis methods

Communication skills

Team collaboration

Problem solving

Education

Bachelor's degree in Computer Science / Electrical Engineering / Computer Engineering / Software Engineering / Aerospace Engineering / Math or Physics

Tools

SQL

Python

R

Scikit-Learn

TensorFlow

PyTorch

Matplotlib

Seaborn

Job description

Garmin seeks a full-time Data Scientist 2 at Garmin's U.S. headquarters in the Greater Kansas City area. You will analyze complex datasets, develop machine learning models, and collaborate with cross-functional teams to provide actionable insights and AI solutions.

Under guidance or with minimal supervision, apply ML, statistics, and data engineering to address challenging business problems and drive data-driven decisions across product strategy and user experiences.