Lead a sub-team of data scientists within the Intelligence Solutions Group to define technical strategy for evaluating and measuring John Deere technology including machines, autonomy, and digital products.
Locations (2)
Urbandale, Illinois, United States
United States
Compensation
$123,804 - $185,700 + Benefits
Responsibilities
Lead a sub-team of data scientists, setting priorities, assigning work, and providing day-to-day technical leadership.
Define the technical strategy for how proving ground evaluates and measures John Deere technology suite, including machines, autonomy features, and digital products.
Take vague ideas from 0 to 1: frame the problem, explore the data, build a working prototype, and carry it through to measurable impact.
Operate independently and self-directed. Set your own priorities within the proving ground's goals, and check in with leadership and stakeholders.
Personally build models, analyses, and data pipelines that measure how John Deere technologies perform in real-world use using John Deere data.
Lead a sub‑team of data scientists within the Technology Intelligence Proving Ground, setting priorities, assigning work, and providing day‑to‑day technical leadership.
Locations (2)
Urbandale, Iowa, United States
Illinois, United States
Compensation
$123,804.00 - $185,700.00 + Benefits
Responsibilities
Lead a sub‑team of data scientists within the Technology Intelligence Proving Ground, setting priorities, assigning work, and providing day‑to‑day technical leadership.
Define the technical strategy for how the proving ground evaluates and measures the performance of the John Deere technology suite.
Design and run structured performance evaluations and benchmarks of John Deere technologies.
Take vague ideas from 0 to 1: frame the problem, explore the data, build a working prototype, and carry it through to measurable business impact.
Operate independently and self‑directed, setting your own priorities within the proving ground's goals.
Senior Manager, Data Science, AI and Advance Analytics for Commercial & Medical
Serve as a hands‑on data science practitioner, actively developing models, writing code, conducting exploratory data analysis, engineering features, validating outputs, and supporting deployment of AI/ML solutions into business workflows.
Develop, validate, and deploy machine learning, generative AI, and advanced analytics solutions to address Commercial and Medical business challenges.
Translate complex business problems into well‑defined data science use cases, technical requirements, analytical approaches, and implementation plans.
Build, evaluate, and monitor analytical models and data pipelines, ensuring reproducibility, scalability, explainability, and alignment with CSL data governance standards.
Support the design and implementation of generative AI and LLM‑based solutions, including retrieval‑augmented generation, knowledge mining, conversational analytics, and agentic workflows.
Requirements
Bachelor’s degree in data science, AI, Computer Science, Statistics, Applied Mathematics, or a related quantitative discipline
This internship focuses on machine learning surrogate models for engineering simulation and measurement. The intern will design, train, and evaluate neural surrogate models to predict engineering responses from simulation or measurement data faster than repeated high-fidelity simulation.
Compensation
(PhD) $51-$56
Responsibilities
Generate parametric engineering datasets using simulation and/or measurement sources, with attention to sampling strategy and to numerical noise in the underlying data.
Design, train, and evaluate neural surrogate models for engineering response prediction.
Investigate how the choice of output representation affects accuracy, particularly across operating regions or frequency ranges where the response varies sharply.
Establish rigorous baselines and quantify accuracy as a function of simulation or measurement budget.
Evaluate LoRA and related parameter-efficient methods for specializing trained models to new problem settings, against full and partial fine-tuning.
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