Data Scientist

Base pay range

$60.00/hr - $70.00/hr

• 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 such as 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.

• Education:
  
  Bachelor’s or Master’s 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.

• Programming & Analysis:
  Python, R, SQL, Power Query, or PySpark.
• Machine Learning & AI: scikit‑learn, Tensor Flow, 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 Big Query).
• Statistics & Modeling:
  Regression, time‑series forecasting, clustering, and anomaly detection.
• Governance & Security:
  Familiarity with NIST data‑handling standards, FedRAMP compliance, and secure data pipelines.

• 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

Mid-Senior level

Contract

Engineering and Information Technology

IT Services and IT Consulting