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