Staff Data Scientist

As a Staff Data Scientist, for John Deere Intelligent Solutions Group, located in Austin, TX or Urbandale, IA, you will lead the design, development, validation, deployment, and production support of scalable data science, AI, and machine learning solutions that address complex business problems and enable data-driven decision making. This role will focus on building robust analytical methods and production-ready capabilities for benchmarking, similarity modeling, causal inference, sequence analysis, impact modeling, and scalable insight generation across large and diverse datasets.
In this role, you will serve as a technical lead across multiple projects, working independently with minimal review while partnering closely with product managers, engineering teams, data engineering, domain experts, and other data scientists. You will translate ambiguous business opportunities into clear analytical requirements, technical roadmaps, validation plans, delivery milestones, and production-ready solutions. Additionally, you will: 

- Lead end-to-end data science initiatives from problem framing and methodology selection through prototype development, validation, deployment, monitoring, and production support.
- Design and build scalable machine learning, statistical, causal inference, and sequence analysis solutions using modern data science and cloud-based platforms.
- Develop production-ready models, features, data pipelines, and analytical workflows using tools such as Python, SQL, Spark, Databricks, feature stores, and cloud platforms.
- Partner with product, engineering, data engineering, agronomy/domain experts, and leadership stakeholders to define use cases, success criteria, analytical requirements, and delivery plans.
- Establish and maintain model validation, performance measurement, monitoring, data quality, documentation, and governance practices to ensure reliable, explainable, and defensible insights.
- Provide technical mentorship to other data scientists and staff members, including guidance on modeling approaches, architecture, code quality, validation methods, and production best practices.
- Manage multiple cross-functional initiatives simultaneously, prioritize work, align dependencies, manage deadlines, communicate risks, and drive delivery of measurable outcomes.
- Communicate complex technical methods, assumptions, limitations, tradeoffs, risks, and business implications clearly to technical and non-technical audiences.
- Stay current on emerging AI, machine learning, data engineering, and analytics technologies, and evaluate where new approaches can improve business outcomes.

VISA Sponsorship is NOT available for this position

Preference is for candidates who are able to work onsite in either Austin, TX or Urbandale, IA, however remote may be considered for strong candidates.

- 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.

- PhD in Statistics, Data Science, Computer Science, Applied Mathematics, Engineering, Agriculture-related fields, or another quantitative discipline.
- Deep experience with causal inference in observational data settings, including DAG-based causal reasoning, treatment/control design, propensity or balancing approaches, doubly robust estimation, sensitivity analysis, or related methods.
- Experience with benchmarking, similarity modeling, cohort construction, embeddings, semantic search, vector databases, or related analytical approaches.
- Experience with sequence modeling or sequence analysis methods such as Markov models, sequence alignment, dynamic time warping, temporal embeddings, transformers for event sequences, survival/time-to-event modeling, or process mining.
- Experience working with agronomic, precision agriculture, machine, field-operation, yield, weather, soil, crop-stage, geospatial, remote-sensing, or satellite imagery datasets.
- Hands-on experience with geospatial analytics tools and libraries such as GeoPandas, H3, Rasterio, GDAL, QGIS, ArcGIS, or related spatial tooling.
- Experience operationalizing data science models using CI/CD, automated testing, feature stores, data quality checks, model monitoring, and production support processes.
- Experience implementing emerging AI technologies, including GenAI, agentic systems, autonomous workflows, RAG, embeddings, foundation model adaptation, evaluation, monitoring, and governance.
- Strong systems thinking, synthesis, software execution planning, and stakeholder storytelling skills.
- Experience in early-stage product, R&D, innovation, or research-oriented environments.
- Experience building customer-facing or product-integrated analytics where outputs must be explainable, defensible, trusted, and actionable.
- Familiarity with John Deere’s Precision Tech Stack or related digital agriculture platforms.
- Background in agriculture, digital farming, agronomy, field operations, customer behavior, or decision-support systems.

-  An advanced degree such as a Master’s or Ph.D. in Statistics, Data Science, Computer Science, Applied Mathematics, Engineering, Agriculture-related fields, or another quantitative discipline is preferred.