Why this role stands out
This hybrid role offers a fantastic opportunity to lead impactful data science initiatives within the defense sector, leveraging cutting-edge technology to enhance national security and fostering significant career growth. You'll thrive here if you possess a strong background in data science, a passion for complex problem-solving, and an active TS/SCI clearance, making this an ideal position to advance your expertise and contribute to critical missions. Apply now to join a leading technology and services integrator and make a real difference.
Quick overview
Job description
Company Description
Founded in 1989, SOSi is among the largest private, founder-owned technology and services integrators in the defense and government services industry. We deliver tailored solutions, tested leadership, and trusted results to enable national security missions worldwide.
Job Description
SOSi is seeking a Data Science Lead 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
- The contractor shall oversee the development, deployment, and evaluation of predictive and prescriptive analytics to ensure alignment with mission objectives and operational needs.
- The contractor shall establish a structured framework to distinguish data science and data engineering tasks, ensuring the effective allocation of resources and seamless collaboration between teams.
- The contractor shall submit the Predictive Analytics Strategy & Implementation Report, detailing model development progress, algorithm validation, and mission integration.
- The contractor shall coordinate to access compute and storage resources required to support large-scale model training and analytics workloads, including GPU-accelerated environments, high-performance object storage, and parallel data processing capabilities.
- The contractor shall fund usage-based compute and storage costs through inter-task chargeback arrangements, as approved by the Government PM.
Qualifications
- Active TS/SCI Clearance.
- Master’s degree in Data Science, Artificial Intelligence, Computer Science, or a related field, or;
- Eleven (11) years of equivalent experience in leading AI/ML-driven analytics programs.
- Personnel must have demonstrated experience in developing, deploying, and managing enterprise-level predictive analytics solutions, ensuring governance compliance, and implementing AI/ML models at scale.
- Experience in mission-focused analytics, data-driven decision support, and integration of statistical and machine learning techniques is required.
- Personnel must have experience overseeing sprint-based Agile development cycles and working within cloud-based data science environments such as AWS, Azure, or Databricks.
- Possess the knowledge and capability to lead the development of predictive and prescriptive analytics, ensuring alignment with mission objectives and the integration of AI/ML-driven insights.
- Proficient in advanced machine learning techniques, statistical modeling, and data science methodologies, with expertise in feature engineering, model validation, and data provenance tracking.
- Experience implementing Agile methodologies, managing analytic sprints, and overseeing collaboration between data science and data engineering teams.
- Strong leadership, strategic planning, and stakeholder engagement skills are required to drive mission-focused analytic innovation.
Preferred Qualifications
- Desirable but not required certifications include AWS Certified Machine Learning – Specialty, Microsoft Certified: Azure AI Engineer Associate, or Certified Analytics Professional (CAP). Additional Information Work Environment
- Normal office conditions
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