Senior Data Scientist
Role details
Job location
Tech stack
Job description
SOSi is seeking a Senior Data Scientist to support mission requirements for a structured approach to further develop, integrate, and sustain a scalable, federated data ecosystem that enhances interoperability, governance, and mission-driven analytics for a DoD customer. The primary objective of the program is to bridge the operational gaps between DoD, IC, interagency, and non-traditional international partners to enable real-time information sharing, dynamic data integration, and mission-tailored analytical capabilities.
Essential Job Duties:
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The contractor shall design and implement advanced ML models and statistical methods to optimize forecasting, risk assessment, and decision-making processes.
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The contractor shall conduct data provenance tracking, ensuring documentation of sources, transformations, and lineage for compliance with governance policies.
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The contractor shall submit the Data Provenance & Lineage Report, summarizing transformation workflows, feature engineering processes, and audit compliance.
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The contractor shall implement sprint-based Agile methodologies, ensuring rapid development cycles, backlog grooming, and alignment with mission requirements.
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The contractor shall provide a Rough Order of Magnitude (ROM) Estimate Report before each analytics project, detailing expected Full-Time Equivalent (FTE) hours, compute costs, storage consumption, and infrastructure requirements.
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The contractor shall conduct quarterly reviews to track cost efficiency, assess system performance, and optimize analytic workflows through the Quarterly Cost & Resource Utilization Report.
Requirements
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Active TS/SCI Clearance.
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Master's degree in Data Science, Machine Learning, Statistics, or a related field, or;
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nine (9) years of equivalent experience in AI/ML model development and deployment.
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Personnel must have demonstrated experience in building and validating AI/ML models using Python, TensorFlow, PyTorch, or Scikit-learn, integrating models into production environments, and optimizing performance for real-time analytics.
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Experience with Databricks, Apache Spark, or similar distributed data processing frameworks is required.
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Experience working with geospatial datasets and integrating AI/ML solutions into mission-critical applications.
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Possess the knowledge and capability to develop advanced machine learning models and optimize analytic workflows for predictive and prescriptive intelligence.
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Proficient in deep learning, supervised and unsupervised learning techniques, data wrangling, and feature engineering.
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Experience with data provenance tracking, model explainability, and bias mitigation in AI/ML applications is required.
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Personnel must be able to translate operational challenges into analytic solutions, ensuring integration of structured, unstructured, and geospatial data.
Preferred Qualifications:
- Desirable but not required certifications include Google Professional Machine Learning Engineer, Microsoft Certified: Azure Data Scientist Associate, or TensorFlow Developer Certification.