Farm Journal
Digital Solutions & AI Manager, Trust In Food™
US
No sponsorshipDetected 1 day ago
JavaScriptPythonSQLMachine LearningData AnalysisData ScienceData VisualizationHubSpotCRMExcelCustomer Support
> stay_score
odds of building a lasting career here
16Unrated
Cap-exempt (no lottery)0
Sponsors this role0
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
No strong sponsorship signal in the public record yet. In the full product we resolve the exact legal entity and show its filing history with a confidence score — treat as unverified until then.
Lottery odds assume a STEM candidate.
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About the role
- Trust In Food has the benefit of engaging directly with the nation's top agri-media brands, trusted for ~150 years in U.S. agriculture.
- Essential Duties and Responsibilities These duties include, but are not limited to, the following.
- EEO statement Farm Journal is an equal opportunity employer.
Nice to have
- Identify opportunities to streamline business processes through technology, including enhancing workflows, automations, dashboards, reporting, and integrations within platforms such as Monday.com and HubSpot.
- Leverage AI technologies to improve event planning, content development and other business processes across Trust In Food.
- Bachelor's degree in computer science, data science, agricultural technology, information systems, or related field
- 6+ years of experience in software development, digital solutions, data analytics, or related technical role, experience in agriculture / ag tech / farm management software / sustainability software preferred
- Proficiency in programming languages (Python, JavaScript, R, or similar)
- Experience with AI/ML tools and platforms (OpenAI API, cloud AI services, or similar)
- Strong skills in data visualization and dashboard development (Tableau, Power BI, Excel, or similar)
- Database knowledge (SQL, data modeling)
Visa & Work Authorization
- Work authorization requirements
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This listing is sourced directly from Farm Journal's careers page and normalized into a canonical job model.