Data Scientist

Raymond James•Saint Petersburg, FL

2d•Hybrid

About The Position

Support the development of advanced decision support systems by employing advanced techniques from data analytics, including statistical analysis and machine learning, particularly including NLP. Develop capabilities that improve the performance of business units and enhance efficiency. Work with business partners to define business use cases utilizing machine learning or advanced analytics. Collect, clean, and analyze data from various heterogeneous data sources. Develop machine learning models for prediction or classification in support of a business use case. Implement advanced AI solutions, such as deep learning. Deploy models to production systems using Docker containers. Write Python modules to automate Data Science tasks. Review Python code of junior colleagues.

Requirements

Master’s degree in Computer Science, MIS, Data Science, or related.
Three (3) years of data science or related experience.
Three (3) years of data science or related experience must include: Data Science; Demonstrated ability explaining machine learning and statistical methods using clear, domain-specific language; Machine learning algorithms, including natural language processing (NLP) techniques; Foundation in statistical methods such as hypothesis testing, confidence intervals, and regression analysis; Programming in Python, with hands-on experience using libraries such as pandas, scikit-learn, NumPy, and either PyTorch or TensorFlow; Writing optimized SQL queries for data extraction and transformation; Developing and deploying predictive models that improve measurable business outcomes; Deploying ML systems to production environments using tools such as Docker, Kubernetes, or cloud platforms (e.g., Azure, AWS, GCP); Working with Agile methodologies, including sprint planning, task estimation, and iterative development.

Responsibilities

Support development of advanced decision support systems by employing advanced techniques from data analytics including statistical analysis, and machine learning, particularly including NLP.
Develop capabilities that improve the performance of the business units and improve efficiency.
Work with business partners to define a business use case utilizing machine learning or advanced analytics.
Collect, clean, and analyze data from various heterogeneous data sources.
Develop machine learning models for prediction or classification in support of a business use case.
Implement advanced AI solutions, e.g., deep learning.
Deploy models to production systems using Docker containers.
Write Python modules to automate Data Science tasks.
Review Python code of junior colleagues.