Data Scientist

Kforce has a client that is seeking a Data Scientist in Phoenix, AZ. Key Responsibilities:

• Data Scientist will develop, test, and optimize machine learning models to solve business challenges and generate actionable insights
• Perform statistical analyses, forecasting, hypothesis testing, and predictive modeling on large and complex datasets
• Partner with business stakeholders to identify opportunities where data science can improve decision-making and operational performance
• Conduct exploratory data analysis to uncover patterns, trends, and opportunities
• Design experiments and evaluate outcomes to support strategic initiatives
• Build and maintain analytical datasets, reports, dashboards, and visualizations
• Present findings and recommendations to both technical and non-technical audiences
• As a Data Scientist, you will support model deployment, monitoring, and performance measurement in production environments
• Collaborate with data engineers, analysts, and technology teams throughout the data science lifecycle
• Stay current on emerging machine learning and AI techniques and recommend practical applications
• Bachelor's degree in Data Science, Statistics, Computer Science, Mathematics, Engineering, or related quantitative discipline
• GIS background
• 5+ years of experience in data science, machine learning, predictive analytics, or a similar role
• Hands-on experience building and validating machine learning models using Python
• Strong understanding of supervised and unsupervised learning techniques
• Experience with statistical analysis, predictive modeling, and data mining methodologies
• Advanced SQL skills and experience working with large datasets
• Experience using visualization and reporting tools such as Power BI
• Excellent communication skills with the ability to translate technical findings into business recommendations

• Master's degree in a quantitative field preferred
• Experience working in AWS, Azure, or other cloud-based environments
• Experience with machine learning frameworks such as Scikit-Learn, TensorFlow, PyTorch, or XGBoost
• Familiarity with predictive analytics, forecasting, optimization, customer analytics, or operational analytics use cases
• Exposure to MLOps concepts, including model deployment, monitoring, CI/CD pipelines, and model lifecycle management