Senior Business Analyst IV – Agriculture

Autonomous Solutions•Clewiston, FL

1d•Onsite

About The Position

The Senior Business Analyst – Agriculture is ASI's embedded data and business analysis lead for one of our most strategic agricultural customers. This person sits with the customer, works shoulder to shoulder with their operations, agronomy, and analytics people, and turns the data coming off ASI's autonomy platform and the customer's farm management systems into decisions that make the operation more efficient and our autonomous system measurably better. This is a hybrid business and technical role, meaning that the work is not building data pipelines, it's understanding an agricultural operation well enough to know which questions matter, pulling together data from systems that were never designed to talk to each other, and assembling the full story - what changed, what it was worth, and what we should do next. Technical ability is required to get at the data and to specify what should eventually be automated, but the value of this role is in the analysis, the judgment, and the ability to influence engineering and business decisions on both sides of the customer relationship.

Requirements

Technical ability is required to get at the data and to specify what should eventually be automated
Understanding an agricultural operation well enough to know which questions matter
Ability to pull together data from systems that were never designed to talk to each other
Ability to assemble the full story - what changed, what it was worth, and what we should do next
Judgment
Ability to influence engineering and business decisions on both sides of the customer relationship
Ability to present findings and recommendations to customer leadership and ASI leadership in clear, decision-ready terms
Ability to reconcile and validate data across systems so the numbers hold up to scrutiny
Ability to document sources, definitions, and known gaps
Ability to connect changes in the autonomous system to measurable operational outcomes
Ability to identify trends and anomalies worth acting on, form hypotheses, and follow them through to a recommendation
Ability to explore higher-order questions as data maturity allows
Ability to quantify labor impact, productivity gains, cost per acre, uptime value, and other drivers of customer value
Ability to translate analysis into business cases and value narratives
Ability to prioritize ruthlessly
Ability to identify manual analysis that has proven its value and should be automated, and specify it clearly for the data platform team to productize
Ability to use modern analytical and AI tooling to move faster
Ability to share what works with the broader team

Responsibilities

Work on site with the customer on a regular cadence, building trusted working relationships with operations leadership, agronomy, equipment and maintenance teams, and the customer's own data analysts.
Act as ASI's on-the-ground analytical partner - surfacing what the operation actually needs, not what we assume it needs.
Bring field and operational learnings back into ASI so product, engineering, and business teams are working from ground truth.
Present findings and recommendations to customer leadership and ASI leadership in clear, decision-ready terms.
Identify what data exists across ASI systems and customer systems, where it lives, who owns it, and how it can be accessed - whether by API, scheduled cloud export, or manual pull.
Coordinate and secure data access with customer stakeholders and third-party platform providers.
Combine ASI autonomy and vehicle data with customer-side sources such as farm management platforms (e.g., John Deere Operations Center), agronomic and yield records, equipment maintenance history, and production data.
Reconcile and validate data across systems so the numbers hold up to scrutiny, and document sources, definitions, and known gaps.
Analyze machine and fleet performance - utilization, acres covered, hours operated, downtime, intervention rates, operating patterns, and efficiency trends.
Connect changes in the autonomous system to measurable operational outcomes, so ASI can tell the difference between improvements that matter and ones that do not.
Identify trends and anomalies worth acting on, form hypotheses, and follow them through to a recommendation.
Explore higher-order questions as data maturity allows, such as the relationship between operating patterns and component wear, or between agronomic inputs and output efficiency.
Partner with ASI business development, product, and finance teams to build and refine Total Cost of Ownership models for agricultural deployments.
Quantify labor impact, productivity gains, cost per acre, uptime value, and other drivers of customer value.
Translate analysis into business cases and value narratives that support customer expansion and market-facing decisions.
Build and maintain the recurring reporting and dashboards that ASI and the customer rely on, partnering with ASI's data platform team on standards and data sources.
Prioritize ruthlessly - pick the highest-value question, answer it well, demonstrate the impact, then move to the next one.
Identify manual analysis that has proven its value and should be automated, and specify it clearly for the data platform team to productize.
Use modern analytical and AI tooling to move faster, and share what works with the broader team.