Applied Data Scientist

Ken Garff Automotive Group•Salt Lake City, UT

About The Position

Ken Garff Automotive Group is looking for a passionate, curious, humble, and hungry Applied Data Scientist to join and help grow Ken Garff’s Data, Analytics, and AI capabilities. Ken Garff truly lives the motto of “We hear you”; this motto applies to all we do within our corporate and regional teams. The Applied Data Scientist will start their role ensuring data is “AI-Ready”. They will ensure clean, relevant, reliable, consistent, and scalable measures are defined and tracked in our holistic data model. Ensure input signal is trustworthy so developed models are not constrained by data quality. They will develop analytical models and data-driven insights that support strategic decision-making and operational improvements. This role analyzes large datasets, builds predictive models, and designs experiments that help the organization better understand customer behavior, operational trends, and business performance. The Applied Data Scientist collaborates with data engineers and the AI team to ensure models are built on reliable data and can be integrated into AI-enabled solutions. The ideal candidate combines strong statistical and machine learning expertise with the ability to translate complex analytical findings into clear insights that drive business outcomes.

Requirements

Applicants must be 18 years or older and be authorized to work in the U.S., have a valid driver license and professional appearance.
All employees must adhere to the below Company Values: Respect: Above everything else Integrity: Do the right thing Growth: One step at a time Humility: Actions speak louder than words Teamwork: Stronger together
Attitude – Demonstrate a positive “can do” attitude; show curiosity and eagerness to learn; be coachable; be “solution-minded” rather than “problem-minded.”
Work Ethic – Dependable, responsible, detail-oriented, and willing to put in the effort to learn new skills and technologies.
Oral Communication – Communicate clearly with team members; listen actively; ask thoughtful questions; respond well to feedback.
Written Communication – Write clearly and informatively; able to document technical work and explain concepts at a basic level.
Professionalism – Approach others with respect and humility; accept responsibility for learning and improvement; follow through on commitments.
Strong foundation in statistics, machine learning, and data analysis.
Proficiency in Python and common data science libraries such as pandas, scikit-learn, and NumPy.
Experience working with SQL and large-scale datasets.
Familiarity with machine learning model development and evaluation techniques.
Knowledge of experimental design and causal inference methods.
Experience building predictive models such as classification, regression, forecasting, or recommendation systems.
Ability to translate complex data findings into actionable business insights.
Experience using data visualization tools and communicating results effectively.
Familiarity with cloud-based analytics environments and data platforms.
Strong problem-solving and analytical thinking skills.
Ability to explain Statsmodels to a non-mathematical audience.
Bachelor’s degree in Data Science, Statistics, Computer Science, Mathematics, or a related quantitative field required; Master’s degree preferred.
4–7 years of experience in data science, analytics, or machine learning roles.
Experience building predictive models, statistical analyses, or machine learning solutions using real-world business datasets.
Strong experience working with Python and common data science libraries such as pandas, scikit-learn, and NumPy.
Experience working with SQL and large-scale data platforms.
Experience applying statistical methods such as regression analysis, forecasting, and experimentation.
Ability to communicate analytical insights clearly to technical and non-technical stakeholders.

Nice To Haves

Experience collaborating with engineering teams to operationalize models or analytics workflows is preferred.

Responsibilities

Coordinates with the business to ensure definitional alignment prior to any analytics.
Ensure data is “AI-Ready” – Clean, Relevant, Reliable, Consistent, Scalable
Develop predictive models and analytics solutions to support business decision-making.
Conduct exploratory data analysis to identify trends, patterns, and opportunities for improvement.
Design and implement statistical models, forecasting systems, and recommendation algorithms.
Evaluate the performance and accuracy of AI and machine learning systems.
Partner with business stakeholders to define analytical questions and interpret results.
Design experiments and A/B tests to evaluate AI-driven initiatives.
Work with data engineering teams to ensure data quality and accessibility.
Support the development of AI applications by providing modeling expertise and evaluation frameworks.
Communicate analytical findings and insights to technical and non-technical audiences.
Maintain documentation and reproducibility of analytical models and experiments.