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Data Scientist - AI/ML Engineering & Machine Learning ModelingBayer AG • United States
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Data Scientist - AI/ML Engineering & Machine Learning Modeling

Bayer AG
  • US
    United States
  • US
    United States
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À propos

Data Scientist ? AI/ML Engineering & Machine Learning Modeling (Contract) Remote
Contract (5 months 28 days)
Published 4 days ago
SQL
LLM - Large Language Model
Python
Job Summary:
We are seeking a highly skilled and hands‑on data scientist who combines deep analytical expertise with strong engineering capabilities to design, build, and deploy AI and machine learning solutions at scale. In this role, you will serve as a bridge between business stakeholders and technical teams, transforming complex business challenges into measurable, production‑ready AI/ML services. You will work across modern cloud platforms to develop everything from generative AI agents to traditional machine learning models, while also creating intuitive applications that deliver insights directly to end users. This position offers the opportunity to work on diverse problems spanning classification, forecasting, recommendation systems, and cutting‑edge generative AI applications, all while contributing to a culture of technical excellence and best practices.
Responsibilities
Collaborate with business and engineering teams to translate business problems into well‑defined AI/ML solutions with clear success metrics
Design, train, validate, and deploy machine learning models for classification, regression, recommendation, and time‑series forecasting applications
Select appropriate algorithms, features, and evaluation strategies aligned with business objectives
Develop and evaluate generative AI agent applications using frameworks such as Langchain and Google ADK
Leverage advanced techniques including retrieval‑augmented generation, prompt engineering, and vector database integration
Build and maintain reliable data pipelines and model inference endpoints across cloud platforms
Implement CI/CD workflows, automated testing, and monitoring systems for machine learning and data projects
Create lightweight dashboards and internal applications to deliver models and insights to stakeholders
Write comprehensive model documentation covering problem formulation, modeling approach, validation methodology, data requirements, and deployment procedures
Promote coding best practices, reproducibility, and knowledge sharing across the global data science organization
Skills
Advanced proficiency in Python and SQL
Experience with scikit‑learn, XGBoost, and LightGBM
Proficiency in at least one deep learning framework (PyTorch or TensorFlow)
Hands‑on experience building generative AI and large language model applications
Expertise in developing agentic workflows
Strong ability to build reliable data pipelines with focus on data quality and lineage
Experience implementing CI/CD for machine learning and data projects using tools like GitHub Actions, Cloud Build, or AWS CodeBuild/CodePipeline
Practical experience developing internal dashboards and applications using Streamlit and/or Plotly Dash
Working knowledge of Google Cloud Platform services including BigQuery, Vertex AI, Cloud Run, and Gemini Enterprise
Working knowledge of AWS services including S3, Lambda, ECS, and SageMaker
Deep understanding of AI/ML algorithms including their intuition, assumptions, statistical theory, computational complexity, and appropriate use cases
Qualifications
Master's degree with 1+ years of experience, or PhD in Data Science, Computer Science, Applied Mathematics, Statistics, Econometrics, or related quantitative field
This is an exceptional opportunity to join a rapidly growing global AI/ML team of over 26 cross‑functional members within a larger data science organization of 70+ professionals. You will work in small, autonomous project teams that foster close collaboration and meaningful impact, while contributing to innovative solutions that support business operations across a major agricultural science company. The role offers significant autonomy, exposure to diverse technical challenges, and the chance to shape best practices in a dynamic, forward‑thinking environment.
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  • United States

Compétences linguistiques

  • English
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