Data Scientist

KDA Consulting Inc. is seeking a highly skilled Data Scientist with AI/ML expertise to support mission-critical programs within the Intelligence Community (IC). This role will focus on leveraging advanced analytics, machine learning, and artificial intelligence to extract insights from large, complex datasets and support data-driven decision-making.

The ideal candidate will have a strong foundation in statistical analysis, machine learning model development, and data visualization, along with the ability to translate complex findings into actionable insights for both technical and non-technical stakeholders.

Machine Learning & AI Development

• Design, develop, and deploy machine learning models to solve complex mission problems
• Build predictive and prescriptive analytics solutions to support operational and strategic decision-making
• Evaluate model performance and continuously improve algorithms through testing and tuning

Data Analysis & Exploration

• Analyze large, structured and unstructured datasets to identify trends, patterns, and anomalies
• Perform data cleansing, feature engineering, and transformation to prepare data for modeling
• Apply statistical techniques to validate hypotheses and support analytical findings

Data Visualization & Communication

• Develop dashboards, visualizations, and reports using tools such as Tableau, Power BI, or Python visualization libraries
• Communicate insights and recommendations clearly to both technical teams and senior leadership
• Translate complex analytical results into actionable business or mission outcomes

Model Deployment & Integration

• Collaborate with data engineers and software developers to operationalize models into production environments
• Integrate machine learning solutions into enterprise systems and workflows
• Support cloud-based model deployment in environments such as AWS or Azure

Collaboration & Agile Delivery

• Work closely with cross-functional teams including engineers, analysts, and mission stakeholders
• Participate in Agile processes including sprint planning, stand-ups, and retrospectives
• Contribute to continuous improvement of data science methodologies and processes

Requirements