Data Scientist

Job Title

Data Scientist

Location

Remote

Clearance Required

Secret Clearance

Position Type

Full-time, W2

About VivSoft

VivSoft is a mission-driven technology company specializing in Cloud, DevSecOps, Artificial Intelligence, and Digital Experience. We are a diverse team of innovators focused on creating open, scalable, and automated solutions that drive digital transformation in federal space. Our work culture fosters collaboration, creativity, and continuous learning.

Job Summary

We seek an experienced Data Scientist to analyze complex, multi-source datasets, build predictive models, and deliver actionable insights to support strategic and operational decision-making. The ideal candidate will have strong statistical and machine learning expertise, hands-on experience with Apache Spark and distributed SQL engines, and the ability to work with modern lakehouse architectures and AI-assisted analytical tools.

Key Responsibilities

• Analyze large, multi-source datasets using statistical methods, including regression, hypothesis testing, probability, and time-series analysis.
• Develop, validate, and deploy distributed machine learning models using Apache Spark and Spark MLlib across large-scale environments.
• Query and analyze lakehouse data using Athena, Trino, and Spark SQL, considering partitioning, file layouts, and query performance.
• Apply LLM-assisted and agentic AI tools to accelerate data exploration while validating findings against statistical baselines.
• Define operational metrics and develop dashboards and data products using Grafana or comparable visualization platforms.
• Design experiments and hypothesis tests to evaluate operational changes, model performance, and analytical outcomes.
• Collaborate with Data Engineers on data requirements and AI Engineers on productionizing models.
• Document analytical methods, assumptions, limitations, and uncertainty, communicating findings to technical and non-technical stakeholders.

Required Qualifications

• Bachelor's degree in a related technical field or equivalent qualifying experience
• Minimum 5 years of professional experience in Data Science, Advanced Analytics, or Statistical Modeling.
• An US Citizen with an active U.S. Government Secret security clearance.
• Strong foundation in statistics, including regression, probability, hypothesis testing, and time-series analysis.
• Hands-on experience developing and running distributed machine learning models using Apache Spark, including Spark MLlib, at cluster scale.
• Proficiency in Python and SQL, with experience using distributed SQL engines such as AWS Athena, Trino, or Spark SQL.
• Understanding of lakehouse architectures, Apache Iceberg or similar open table formats, data partitioning, and file-layout optimization.
• Experience using LLM-assisted or agentic AI tools for analytical workflows, with the ability to independently validate generated results.
• Experience creating dashboards and metrics using Grafana or comparable visualization tools.
• Bachelor’s degree in computer science, Information Systems, Engineering, Data Science, or a related technical field. Additional relevant experience may substitute for education requirements on a year-for-year basis.

Preferred Qualifications

• Experience with AWS services, including S3, EMR, Athena, and Glue.
• Familiarity with Apache Airflow and data workflow orchestration.
• Experience with Bayesian methods, risk scoring, security analytics, or vulnerability analytics.
• Experience with Kubernetes-based data science workflows.
• Master's degree in Statistics, Mathematics, or another quantitative discipline.
• Previous Department of Defense (DoD) or Federal Government analytics experience.

Benefits

• Comprehensive Medical, Dental, and Vision Plans (Healthcare benefits are 100% employer-paid for employees only)
• Life Insurance
• Paid Time Off (Flexible/Combined PTO, Bereavement Leave, 11 Company Paid Holidays)
• 401K Retirement Plan with employer match
• Professional Development Training Reimbursement

Salary Range

$135K-$145K per annually