Data Scientist

Position: Data Scientist

Location: Onsite – Boca Raton, FL

Duration: 6 Month CTH

Rate: $65/hr C2C

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REQUIRED SKILLS

Machine Learning & AI Architecture:

• Design and architect scalable enterprise AI/ML solutions. Define feature engineering strategies and select the best algorithms to solve complex business problems.

Model Training & Optimization:

• Develop efficient training pipelines and implement distributed training methodologies. Optimize model performance using advanced hyperparameter tuning techniques.

MLOps & Production Deployment:

• Deploy ML models into production using CI/CD pipelines, Docker, and Kubernetes. Ensure scalability, security, and reliability across cloud environments.

Model Governance & Monitoring:

• Establish model validation frameworks, fairness testing, and bias audits. Implement monitoring, drift detection, retraining workflows, and lifecycle management.

Python & SQL Development:

• Write production-grade Python and SQL code for large-scale data environments. Optimize data processing, query performance, and overall system efficiency.

• Be responsible for designing machine learning and AI models by framing business problems, engineering features, and selecting appropriate algorithms and architectures. When designing solutions create processes that can be utilized for multiple business reasons and is adaptable. Responsible for larger more complex business problems that are multi-dimensional.
• Train models by preparing data, fitting algorithms, tuning hyperparameters, and validating robustness through cross-validation techniques. Prior to development able to articulate the trade-off on different modeling techniques and implications when applied to business problem.
• Validate model performance using statistical metrics, conduct fairness and bias assessments, and perform error analysis to refine model quality. Create evaluation metrics and results that tie to business outcomes. Able to articulate how model performance gain equates to business value.
• Deploy models into production environments by packaging them appropriately, integrating with systems, automating deployment workflows, including robust error handling and documenting for maintainability.
• Deploy models into production environments by packaging them appropriately, integrating with systems, automating deployment workflows, including robust error handling and documenting for maintainability.
• Monitor deployed models by tracking performance over time, detecting data drift, triggering retraining when necessary, and implementing logging and alerting mechanisms.
• Working with junior team members to discuss trade-offs and solutions for team members business problems. Provide thought leadership on different ways to advance the business utilizing machine learning and AI.
• Communicate model results and trade-offs to leadership and stakeholder.