SOSi

Senior Data Scientist

Rol remoto de Data Science con fit claro de ubicación del candidato.

Publicado19 jun 2026

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Señal de senioritySenior

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Actualidad de la fuente19 jun 2026

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Match de stackAzure, Python

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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.
The contractor shall conduct data provenance tracking, ensuring documentation of sources, transformations, and lineage for compliance with governance policies.
The contractor shall submit the Data Provenance & Lineage Report, summarizing transformation workflows, feature engineering processes, and audit compliance.
The contractor shall implement sprint-based Agile methodologies, ensuring rapid development cycles, backlog grooming, and alignment with mission requirements.
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.
The contractor shall conduct quarterly reviews to track cost efficiency, assess system performance, and optimize analytic workflows through the Quarterly Cost & Resource Utilization Report.

Active TS/SCI Clearance.
Master’s degree in Data Science, Machine Learning, Statistics, or a related field, or;nine (9) years of equivalent experience in AI/ML model development and deployment.
nine (9) years of equivalent experience in AI/ML model development and deployment.
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.
Experience with Databricks, Apache Spark, or similar distributed data processing frameworks is required.
Experience working with geospatial datasets and integrating AI/ML solutions into mission-critical applications.
Possess the knowledge and capability to develop advanced machine learning models and optimize analytic workflows for predictive and prescriptive intelligence.
Proficient in deep learning, supervised and unsupervised learning techniques, data wrangling, and feature engineering.
Experience with data provenance tracking, model explainability, and bias mitigation in AI/ML applications is required.
Personnel must be able to translate operational challenges into analytic solutions, ensuring integration of structured, unstructured, and geospatial data.

nine (9) years of equivalent experience in AI/ML model development and deployment.

Desirable but not required certifications include Google Professional Machine Learning Engineer, Microsoft Certified: Azure Data Scientist Associate, or TensorFlow Developer Certification.