Job Description
This is an onsite role located in OKC. Applicants must be legally authorized to work in the United States. We do not provide sponsorship for employment visas now or in the future. Benefits: Fuel Your Growth with Love's - company funded tuition assistance Paid Time Off 401(k) – 100% Match up to 5% Medical/Dental/Vision Insurance the first of the month after 30 days Competitive Pay Career Development * Welcome to Love's: The Data Scientist will perform a variety of analytical tasks and projects that support high-impact initiatives across Love’s Fleet Sales and Financial Services organizations. This role sits within Enterprise Analytics and plays a critical role in uncovering customer insights, informing go-to-market and marketing strategies, and enabling data-driven decision-making for products and services that deliver value to trucking companies. This position operates as a hybrid analyst and data scientist, combining advanced analytics, modeling, and business insight to support initiatives such as customer segmentation, pricing and offer optimization, churn and retention analysis, and revenue growth strategies. This is a unique opportunity to work on highly visible, strategic problems with direct impact on Love’s B2B growth and long-term customer relationships. Job Functions Drive Strategic Decision-Making Build analyses and models to support Fleet Sales and Financial Services strategy, including customer acquisition, retention, and growth Partner with Fleet Sales, Financial Services, and Marketing teams to identify opportunities to improve customer value and commercial performance Analyze B2B customer behavior, purchasing patterns, and product usage to inform strategic decisions Support evaluation of new products, services, pricing strategies, and program performance Develop Advanced Analytics Solutions Design and deploy predictive models (forecasting, segmentation, optimization) Utilize statistical languages (R, Python, SAS) to manipulate data and draw insights from large data sets Apply statistical and machine learning techniques to solve complex business problems Enable AI-driven workflows and scalable decision-support tools Develop processes and tools to monitor and analyze model performance and data accuracy Enable Marketing & Growth Strategies Support B2B marketing strategy through campaign measurement, attribution, and targeting analytics Partner with marketing teams to optimize messaging, offers, and customer engagement strategies Build dashboards and performance tracking tools to monitor key KPIs related to revenue, customer growth, and engagement Help align sales and marketing efforts through shared data and insights Improve Efficiency Through Automation Streamline and automate reporting and analytical workflows Build scalable data products that reduce manual effort and improve speed to insight Communicate Insights Clearly Develop a deep understanding of Love’s business, including products, customers, and performance drivers Coordinate with stakeholders from Fleet Sales, Financial Services, and Marketing to gather requirements, develop models as appropriate, and provide actionable insights Translate complex data into clear, actionable recommendations for business leaders Create compelling visualizations and presentations for cross-functional stakeholders Deliver insights and recommendations in a clear, concise manner using meaningful visualizations that allows for business understanding and action Experience and Qualifications: BA/BS minimum, Masters preferred, in a relevant field, such as business analytics, mathematics, statistics, finance, economics, management information systems, computer science, engineering or other quantitative fields. 3+ years experience preferred in analytics, data science or related field Experience with data analysis, statistical modeling, or machine learning Skills and Physical Demands: Proficiency querying data from relational databases using SQL Competency using R, Python, or other similar statistical software to develop analytical solutions Experience with data wrangling, data cleaning and prep Awareness of Big Data concepts, tools, and architecture Data modelling and business analytics experience Excel proficiency including simple formulas and pivot tables Understanding of data warehouse princip