Specialist, Yield Management - Gtm Aa Data Scientist

Ford Ford · Auto · Dearborn, MI +1 · Global Data Insight & Analytics

Data Scientist role focused on building and integrating LLM-powered workflows and agentic systems for business insights and decision support, while also developing data pipelines and evaluating models. This is a hybrid role spanning data engineering, machine learning, and applied AI.

What you'd actually do

  1. Design, train, and evaluate machine learning models, including predictive, classification, and ensemble methods, and conduct exploratory data analysis to surface trends, anomalies, and decision-support signals
  2. Build and integrate LLM powered workflows for insight generation and decision support, blending structured business metrics with external signals through effective prompt engineering and harness in the agent
  3. Design, build, and maintain scalable ETL and data pipelines across multi-source datasets to power analytics, reporting, and downstream applications
  4. Develop interactive analytics applications and dashboards (such as Dash/Power BI) that deliver real-time analytics, KPI monitoring, and actionable business insights
  5. Establish model evaluation frameworks grounded in statistical metrics and business KPIs, and safeguard data reliability through validation of completeness, consistency, and ongoing pipeline monitoring

Skills

Required

  • Python
  • SQL
  • Machine learning algorithms
  • Statistical methods
  • Model evaluation techniques
  • Structured data
  • Unstructured data

Nice to have

  • Master's degree in a quantitative field
  • Cloud platforms (GCP, AWS, Azure)
  • Generative AI
  • Large Language Models (LLMs)
  • Prompt engineering
  • AI agent frameworks
  • Data pipeline and engineering tools (PySpark, Airflow, BigQuery)
  • Data visualization tools (Power BI, Tableau, Dash)
  • Communication skills
  • Inquisitive, proactive mindset

What the JD emphasized

  • 3+ years of hands-on experience applying Python and SQL to data analysis and machine learning
  • Solid understanding of core machine learning algorithms, statistical methods, and model evaluation techniques
  • Demonstrated experience working with both structured and unstructured data
  • Exposure to Generative AI, Large Language Models (LLMs), prompt engineering, or AI agent frameworks

Other signals

  • LLM integration
  • agent frameworks
  • data pipelines
  • model development