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Experience designing, building, evaluating, or deploying agentic AI systems, AI assistants, workflow automation, or LLM-based applications.
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Strong proficiency in causal inference, including experience with experimental design, quasi-experimental methods, treatment effect estimation, or causal modeling.
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Experience with geospatial data science, including spatial analytics, geospatial feature engineering, remote sensing analysis, satellite imagery, or precision agriculture datasets.
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Experience working with Databricks, Apache Spark, distributed computing, and large-scale data processing environments.
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Experience building production-quality analytical workflows, reusable data products, or scalable data science pipelines.
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Familiarity with cloud-based data platforms, model development environments, version control, and collaborative software development practices.
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Experience working as part of a digital product team, including collaboration with product managers, engineers, designers, domain experts, and business stakeholders.
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Ability to translate stakeholder needs into analytical questions, technical requirements, prototypes, and actionable insights.
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Experience creating interactive dashboards, data applications, or visualization tools that support decision-making.
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Knowledge of agricultural, geospatial, machine telemetry, IoT, or digital product data is a plus.