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# Lead Data Analyst

- **Company:** Bayer AG
- **Location:** St. Louis, MO, United States (Remote available)
- **Experience:** Expert
- **Salary:** $124,000.0 - $140,000.0
- **Contract:** Permanent contract
- **Skills:** Geographic Information Systems, Application Programming Interfaces (APIs), Airflow, Amazon S3, BigQuery, Continuous Integration, Python (Programming Language), NumPy, Power BI, Tensorflow, SQL Databases, Tableau (Software), Keras, Gitlab, Pandas, Matplotlib, Pyspark, Scikit Learn, Data Analytics, Streamlit Framework, Data Pipelines, Docker
- **Published:** November 1, 2026
- **Apply:** https://www.financialjobbank.com/job.asp?id=3477601366&tx=IT9083TTI&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

Lead Data Analyst for St Louis, MO to lead data analytics tasks by defining, prioritizing & setting timelines to deliver agricultural data insights and analytic requests and research projects; define research problems & develop strategic planning for agronomic data analytics projects; understand customer pain points & implement ways to improve workflows; serve as technical lead for architectural decisions; lead the building & implementation of operational models, data pipelines, statistical analyses and business intelligence tools to derive insights from user behavior data and improve business performance; advise global teams on data processes & systems; mentor junior team members on data, analytics and visualization. Requires Master's in C.S., Applied Mathematics, Operations Research or closely-related quantitative field & 3 yrs experience in an agricultural, geospatial and/or biotechnology environment: conducting data manipulations and analysis of geospatial and/or agronomic
datasets; using SQL, PySpark, BigQuery & source APIs to evaluate agronomic, plant biotech and/or geospatial data; conducting exploratory & statistical data analysis to evaluate data trends using Python, including Numpy, Pandas, Matplotlib and Seaborn; applying statistical libraries including Scikit-learn, Tensorflow and Keras to geospatial and/or agronomic data; analyzing agronomic and/or geospatial datasets using WKT, Shape and GeoJSON; performing geospatial data manipulation & visualization; developing Tableau, PowerBI and Streamlit dashboards & user interfaces to track customer and internal model delivery among cross-functional product teams; creating & managing continuous integration and deployment of automated data pipelines to develop datasets for operational model ingestion; using Docker, Airflow, S3 and GCS storage to build and support automation workflows ; using Gitlab to store & distribute codebase; building & deploying CI/CD pipelines to automate, build, test and deploy