Data Scientist

Fredericksburg, VA - USA

Monthly Salary: Not Disclosed

Posted on: 30+ days ago

Vacancies: 1 Vacancy

Job Summary

General Responsibilities:

Design and implement data pipelines analytics models and machine-learning solutions to support process automation and predictive decision-making.
Collect clean and validate structured and unstructured data from multiple enterprise systems ensuring data quality and integrity.
Perform statistical analysis forecasting and pattern recognition to identify process improvement opportunities.
Develop interactive dashboards and visualizations using tools like Power BI or Tableau for real-time performance tracking.
Collaborate with Solution Architects and Business Analysts to integrate data insights into workflow automation and digital solutions.
Support the creation and enforcement of data governance metadata and access-control frameworks in coordination with cybersecurity policies.
Evaluate and deploy AI/ML models (e.g. classification regression clustering NLP) within automation and decision-support platforms.
Implement ETL processes and maintain scalable data storage on cloud platforms such as AWS Azure or GCP.
Provide technical documentation data dictionaries and model explainability reports for audit and compliance purposes.
Support training and knowledge transfer enabling end users to interpret and leverage analytical insights.

Minimum Qualifications:

Education: Bachelors or Masters degree in Data Science Computer Science Statistics Engineering or related discipline.
Experience:
- 5 8 years of experience in data analytics machine learning or AI model deployment.
- Proven success designing analytical solutions for IT modernization automation or enterprise transformation projects.
- Experience working with large complex datasets and applying data-driven methods to improve business or operational performance.

Technical Expertise:

Programming & Analysis: Python R SQL Power Query or PySpark.
Machine Learning & AI: scikit-learn TensorFlow Keras or PyTorch.
Data Visualization: Power BI Tableau Matplotlib or Plotly.
Data Engineering: ETL tools APIs REST services and cloud data storage (AWS S3 Azure Data Lake GCP BigQuery).
Statistics & Modeling: Regression time-series forecasting clustering and anomaly detection.
Governance & Security: Familiarity with NIST data-handling standards FedRAMP compliance and secure data pipelines.

Preferred Certifications:

Microsoft Certified: Data Scientist Associate or Azure AI Engineer
Google Professional Data Engineer or AWS Certified Data Analytics Specialty
Certified Analytics Professional (CAP) INFORMS
Lean Six Sigma Green Belt (for data-driven process improvement)
Power BI Data Analyst Associate

General Responsibilities: Design and implement data pipelines analytics models and machine-learning solutions to support process automation and predictive decision-making. Collect clean and validate structured and unstructured data from multiple enterprise systems ensuring data quality and integrit...

General Responsibilities:

Design and implement data pipelines analytics models and machine-learning solutions to support process automation and predictive decision-making.
Collect clean and validate structured and unstructured data from multiple enterprise systems ensuring data quality and integrity.
Perform statistical analysis forecasting and pattern recognition to identify process improvement opportunities.
Develop interactive dashboards and visualizations using tools like Power BI or Tableau for real-time performance tracking.
Collaborate with Solution Architects and Business Analysts to integrate data insights into workflow automation and digital solutions.
Support the creation and enforcement of data governance metadata and access-control frameworks in coordination with cybersecurity policies.
Evaluate and deploy AI/ML models (e.g. classification regression clustering NLP) within automation and decision-support platforms.
Implement ETL processes and maintain scalable data storage on cloud platforms such as AWS Azure or GCP.
Provide technical documentation data dictionaries and model explainability reports for audit and compliance purposes.
Support training and knowledge transfer enabling end users to interpret and leverage analytical insights.

Minimum Qualifications:

Education: Bachelors or Masters degree in Data Science Computer Science Statistics Engineering or related discipline.
Experience:
- 5 8 years of experience in data analytics machine learning or AI model deployment.
- Proven success designing analytical solutions for IT modernization automation or enterprise transformation projects.
- Experience working with large complex datasets and applying data-driven methods to improve business or operational performance.

Technical Expertise:

Programming & Analysis: Python R SQL Power Query or PySpark.
Machine Learning & AI: scikit-learn TensorFlow Keras or PyTorch.
Data Visualization: Power BI Tableau Matplotlib or Plotly.
Data Engineering: ETL tools APIs REST services and cloud data storage (AWS S3 Azure Data Lake GCP BigQuery).
Statistics & Modeling: Regression time-series forecasting clustering and anomaly detection.
Governance & Security: Familiarity with NIST data-handling standards FedRAMP compliance and secure data pipelines.

Preferred Certifications: