Data Scientist

- 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.

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.

- 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.

- 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.

- 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.

- 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 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.

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.

- 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.

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.