Mid-Level Data Scientist

Key Responsibilities

• Apply semantic and vibe coding efficiencies to uncover patterns and thematic insights from structured and unstructured data.
• Translate complex data into clear, actionable narratives through data storytelling and visualization.
• Design, implement, and maintain AI approaches (such as product similarity) and predictive models (supervised and unsupervised) for anomaly detection, classification, regression, and pattern recognition.
• Congduct rigorous model evaluation using precision, recall, F1 score, AUC, and other performance metrics to ensure reliability and fairness.
• Demonstrate an innovative mindset by exploring emerging technologies, creative approaches, and novel methodologies to solve complex problems efficiently.
• Utilize modern efficiency tools and frameworks such as GitHub Copilot, automated pipelines, and CI/CD to streamline development and deployment.
• Optimize workflows for scalability in cloud environments (Azure).
• Work collaboratively within Agile teams, contributing to iterative development, shared problem-solving, and continuous improvement.
• Communicate complex analytical findings clearly to both technical and non-technical stakeholders.
• Maintain documentation and leverage Git-based version control for collaborative development.

Required Qualifications

• Statistical & AI/ML Modeling: 3+ years developing statistical and AI/ML models using leading-edge tools and best practices.
• Predictive Analytics: 3+ years building regression, classification, NLP, and other statistical/ML models.
• Programming: 3+ years of hands-on experience with Python (Pandas, Scikit-learn, PyTorch/TensorFlow for deep learning).
• Cloud: 3+ years working in modern cloud environments (Azure); Certifications advantageous.
• SQL: 2+ years of experience with SQL for data manipulation and querying.
• Visualization: Proficiency in dashboard visualization tools such as Power BI or equivalent.
• Semantic Coding: Proven ability to integrate semantic coding techniques into ML workflows.
• Education: Bachelor's, PhD, or equivalent professional experience in Data Science Machine Learning, Computer Science, Mathematics, or related field.

Preferred Qualifications

• Experience with Azure ML, Azure Synapse, or Azure Data Lake Storage.
• Familiarity with NLP pipelines and text classification applied to unstructured data.
• Experience in public sector or regulated environments.
• Azure certifications in data science or AI are a plus.

Other Qualifications

• Strong oral and written communication skills for data storytelling with diverse audiences.
• Demonstrated experience with Git-based version control and collaborative development practices.
• Eagerness to learn and adopt new technologies, while actively contributing to team knowledge-sharing.
• Must be able to pass a Federal agency background check and obtain a government-issued ID badge before starting work.