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The Data & Analytics Specialist plays an important role in executing data analysis and delivering insights to support Kia America’s Mobility and Remarketing business. This role leverages large, diverse datasets that inform business decisions and improve operational performance. The Specialist partners closely with Kia North America’s Big Data team and other affiliate data analytics teams to develop models to support the department’s analytical needs. This role is responsible for developing and maintaining analytical datasets, dashboards, and reporting tools, as well as performing statistical and financial analysis to support business opportunities. A strong foundation in data analytics and statistics are necessary to ask the right questions, interpret complex data, and translate it into meaningful insights. The position focuses on addressing ambiguous business questions, developing predictive and analytical models, and clearly communicating findings to stakeholders. Beyond standard reporting, the Specialist is expected to frame problems, design analyses, and provide actionable recommendations that influence business decisions.

Priority One – 45%

Business Problem Framing, Insights, and Analytics:

• Understand business processes and decision frameworks and translate them into data-driven KPIs.
• Analyze ambiguous Fleet & Mobility business challenges (pricing, volume, channel optimization), define success metrics, and deliver decision-ready recommendations.
• Identify key drivers and trends tied to valuation and channel performance.
• Translate complex analysis into business-relevant insights that non-technical stakeholders can act on.
• Analyze large amounts of data to discover trends and patterns.
• Analytics will be primarily focused on remarketing, Certified Pre-Owned vehicles (CPO), branded products, fleet, and other retail and wholesale sales channels.

Priority Two – 30%

Data, Reporting, and Dashboard Management

• Assess the accuracy of new data sources
• Build prediction and classification models
• Coordinate with Kia North America Big Data team for data and modeling development
• Curate and preprocess structured and unstructured internal/external data.
• Maintain/monitor daily/weekly data feeds, published reporting, and dashboards supporting auction and other operations.
• Developing/maintaining domain analytical models and decision tools that drive better business outcomes.
• Develop and distribute reporting and databases on key aspects of the Fleet & Mobility business. 
• Manage digital sales platforms.

Priority Three – 25%

Partner Management and Tool Development:

• Leverage operating systems and third-party software, to assist in the management of digital sales platforms, administrative platforms, dashboards, and other tools to support the business. 
• Coordinate with third party data partners and Kia North America for data integration into Big Data model.
• Leverage the enterprise big data platform and work closely with the Big Data Analysis team; Big Data owns enterprise data engineering, governed datasets, and shared platform capabilities (clusters/pipelines/tooling).
• Collaborate with Big Data to deliver model outputs and analytical datasets into downstream tools (dashboards/apps/partner systems) and translate business needs into technical requirements for deployments/feeds.
• Assist management in development and preparation of financial tools for transactional components of the business, such as fleet incentives, remarketing pricing, Total Cost of Ownership (TCO).
• Support special or ad-hoc projects as needed.

Personally Performed Duties

• Conduct analytics supporting remarketing, Certified Pre‑Owned (CPO), branded products, fleet, and other retail and wholesale sales channels.
• Collaborate with the Kia North America Big Data Analysis team on predictive modeling, analytical development, and sales division initiatives.
• Translate business and reporting requirements into well‑defined analytical specifications and data models.
• Prepare, analyze, and present key performance indicators for senior management; monitor performance as directed.
• Partner with Finance, Incentives, and Product Planning teams on analytics related to pricing, residual values, and fleet incentives.
• Manage and support digital sales platforms, data feeds, dashboards, and recurring reports.
• Develop and maintain data integrations, reporting tools, and analytical dashboards.
• Document analytical methodologies and results; support special projects and other duties as assigned.

• Bachelor’s degree or equivalent experience required; major in a quantitative field preferred  (e.g., Data Science, Statistics, Computer Science, Engineering, Economics, Mathematics, Business Analytics, or related field).
• Master’s degree in Business Administration, Information Systems, or Finance preferred.

Related Experience:

• 3-5 years of professional experience required.

Directly Related Experience:

• Minimum 1 year of experience in analytics, business intelligence, data science or related field. 3+ years of experience preferred.
• Strong SQL proficiency required. Strong data analysis and statistical foundations required.
• Must be proficient in data or reporting tools such as Power BI or the equivalent. Strong business analytics skills such as fluency in financial statements and economics.
• Familiarity with applied machine learning concepts and big data processing frameworks required.
• Python proficiency preferred.
• Experience managing digital sales platforms strongly preferred.
• Experience working with large datasets, including cleaning, transforming, and validating data.
• Experience with predictive analytics (trend forecasting, regression models) to support business planning and decision-making.
• Experience partnering with data/engineering teams to define data requirements and support/validate reporting or tool development.
• Experience maintaining recurring reporting, dashboards, and data feeds.

• Proficiency in Python and SQL
• Effective presentation skills for communicating analytical findings to management audiences.
• Knowledge of a variety of machine learning techniques, deep learning a plus
• Knowledge of advanced statistical techniques
• Proficiency with common Python libraries for data analysis such as Pandas and NumPy
• Ability to structure ambiguous business problems and define clear metrics/KPIs.
• Strong data visualization and dashboarding skills (e.g. Power BI, MicroStrategy, Tableau).
• Strong business acumen and ability to communicate insights clearly to non-technical stakeholders.
• Proficiency with visualization libraries such as Matplotlib, Seaborn, Plotly, Bokeh and plotnine
• Ability to develop and evaluate statistical and machine learning models using libraries such as stats models and scikit-learn
• Exposure to big data processing tools such as Spark (e.g., PySpark), especially Hadoop ecosystem or cloud platforms.
• Knowledge of deep learning frameworks such as PyTorch and TensorFlow preferred.
• Strong data-driven problem-solving skills .