Staff Data Scientist

John Deere John Deere · Industrial · Austin, TX +1 · Data and Analytics (CA)

Staff Data Scientist role focused on leading the design, development, validation, deployment, and production support of scalable data science, AI, and machine learning solutions. The role involves building robust analytical methods and production-ready capabilities for various modeling techniques (benchmarking, similarity, causal inference, sequence analysis, impact modeling) using modern data science and cloud platforms. Responsibilities include technical leadership, translating business problems into analytical requirements, developing production-ready models and pipelines, and mentoring other data scientists.

What you'd actually do

  1. Lead end-to-end data science initiatives from problem framing and methodology selection through prototype development, validation, deployment, monitoring, and production support.
  2. Design and build scalable machine learning, statistical, causal inference, and sequence analysis solutions using modern data science and cloud-based platforms.
  3. Develop production-ready models, features, data pipelines, and analytical workflows using tools such as Python, SQL, Spark, Databricks, feature stores, and cloud platforms.
  4. Partner with product, engineering, data engineering, agronomy/domain experts, and leadership stakeholders to define use cases, success criteria, analytical requirements, and delivery plans.
  5. Establish and maintain model validation, performance measurement, monitoring, data quality, documentation, and governance practices to ensure reliable, explainable, and defensible insights.

Skills

Required

  • 5 or more years of relevant technical experience in data science, machine learning, analytics, AI, product innovation, or related technical roles.
  • 1 or more years of experience providing technical leadership on one or more projects, including mentoring or guiding staff members on technical work.
  • Strong hands-on experience building, validating, and deploying machine learning, statistical, or AI models using Python or related object-oriented programming approaches.
  • Strong experience using SQL and large-scale data platforms such as Spark, Databricks, or similar technologies to query, transform, and analyze large datasets.
  • Experience designing and delivering production-ready data science solutions, including reusable code, testing, documentation, monitoring, and production support practices.
  • Demonstrated ability to manage multiple cross-functional initiatives simultaneously, including planning, prioritization, stakeholder alignment, dependency management, deadline management, and delivery tracking.
  • Ability to work independently in ambiguous or rapidly changing environments and translate loosely defined business problems into structured analytical plans and measurable deliverables.
  • Strong foundation in statistics, experimental design, observational study design, model validation, uncertainty, bias, confounding, robustness, stability, drift, and business relevance.
  • Experience collaborating with product, engineering, data engineering, domain experts, and business stakeholders to define requirements and deliver data-driven solutions.
  • Excellent communication skills, including the ability to explain technical methods, tradeoffs, assumptions, risks, results, and recommendations to both technical and non-technical stakeholders.
  • Experience using data in digital solutions and meaningfully collaborating with technical experts across functions.
  • Demonstrated ability to mentor others, influence technical direction, establish best practices, and raise the quality of data science delivery across a team.

Nice to have

  • PhD in Statistics, Data Science, Computer Science, Applied Mathematics, Engineering, Agriculture-related fields, or another quantitative discipline.
  • Deep experience with causal inference in obs

What the JD emphasized

  • production support
  • production-ready capabilities
  • production-ready models
  • production best practices
  • production-ready data science solutions
  • production support practices

Other signals

  • building robust analytical methods
  • production-ready capabilities
  • scalable insight generation
  • lead end-to-end data science initiatives
  • develop production-ready models, features, data pipelines, and analytical workflows