Sr. Data Scientist

Bayer•St. Louis, MO

5d•$120,000 - $150,000•Remote

About The Position

We are seeking a Sr. Data Scientist to join our team in St. Louis, MO. In this role, you will develop predictive agronomic models to address scientific challenges in precision agriculture. Your responsibilities will include managing data preparation and cleansing for multi-source agronomic data, ensuring quality standards, and building reliable pipelines for integration and analysis. You will lead the implementation of research projects using statistical, machine learning, and optimization techniques. Additionally, you will review and revise code to ensure quality and reproducibility, provide mentorship and technical guidance to junior data scientists, and offer coaching and training in agile framework and methodologies. You will also coordinate sprints, retrospective meetings, and stand-ups.

Requirements

Master's in Data Science, Statistics, Applied Mathematics, or closely-related quantitative field
4 years of experience building predictive machine learning, statistical models, and optimization algorithms for research applications
Experience deploying code to AWS
Writing code in Python with data science packages, including Pandas, Numpy & Geopandas for data aggregation
Using Matplotlib & Seaborn for data analysis & visualization
Using PyMC for Bayesian statistical modeling & inference
Using Sklearn & XGboost for machine learning and modeling
Using TensorFlow and Keras for deep learning modeling
Working with large, complex datasets across agronomic, genetics, environmental & weather domains using SQL, PySpark and Google BigQuery
Designing & analyzing agronomic trial data, including experimental protocols and hypothesis testing to evaluate outcomes
Using version control systems, including GitLab, to track code history & perform code review
Creating & deploying CI/CD pipelines, applying MLOps best practices, including code documentation, merge/pull requests, unit testing, and code modularization
Using JIRA & Agile methodologies for task prioritization and project management
Communicating with technical & non-technical stakeholders at all levels of the organization, including business stakeholders, agronomists, data engineers, and data scientists

Responsibilities

Develop predictive agronomic models to address scientific challenges in precision agriculture
Manage data preparation and cleansing for multi-source agronomic data
Ensure quality standards & build reliable pipelines for integration and analysis
Lead implementation of research projects using statistical, machine learning & optimization techniques
Review & revise code to ensure quality and reproducibility
Provide mentorship and technical guidance to junior data scientists
Provide coaching & training in agile framework and methodologies
Coordinate sprints, retrospective meetings & stand-ups