Machine Learning Scientist
On-siteFlorida, United States or Dallas, Florida, United States
Job Summary
Research, design, and deploy advanced machine learning models including supervised, unsupervised, and deep learning techniques to solve complex business problems. Analyze large structured and unstructured datasets to build feature engineering strategies and predictive pipelines, while collaborating with Data Engineers on data quality and accessibility. Execute experiments, A/B tests, and hypothesis frameworks to validate model effectiveness and generate actionable insights for stakeholders. Partner with Machine Learning Engineers to integrate models into production, manage MLOps pipelines, and ensure reliability through monitoring and retraining. Translate technical findings into scalable AI solutions across Product, Operations, and Sales teams, adhering to responsible AI governance and compliance standards.
Required Qualifications
- Master's degree or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, Statistics, Mathematics, Data Science, Engineering, or a related quantitative field
- 3+ years of experience in machine learning, data science, AI research, or related disciplines
- Strong proficiency in Python and machine learning frameworks
- Experience with machine learning libraries such as TensorFlow, PyTorch, Scikit-learn, XGBoost, or similar tools
- Strong knowledge of statistics, probability, and predictive modeling techniques
- Experience working with large datasets and data analysis tools
- Familiarity with SQL and data querying technologies
- Strong analytical thinking and problem-solving abilities
- Excellent communication and presentation skills
- Must currently reside in one of the approved locations listed above
Desired Qualifications
- Ph.D. in Machine Learning, Artificial Intelligence, Statistics, or related field
- Experience with Deep Learning, NLP, Computer Vision, or Generative AI
- Experience with Large Language Models (LLMs) and AI research
- Familiarity with cloud platforms such as AWS, Azure, or GCP
- Experience with MLOps, model deployment, and production AI systems
- Knowledge of Spark, Databricks, Hadoop, or distributed computing frameworks
- Experience publishing research papers, patents, or technical publications
- Experience with reinforcement learning or advanced optimization techniques
- Familiarity with AI governance, model explainability, and responsible AI frameworks
- Experience working in highly regulated industries such as finance, healthcare, or insurance
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