Job Opportunities API

 Who we are: Tria Federal delivers digital services and technology solutions that support the health and safety of veterans, service members and civilians. For two decades, federal agencies have relied on Tria companies to advance their critical missions and modernize their systems, so that they can uphold their commitment to the American people. Today, we are pushing the boundaries of possibility through partnerships and investments in artificial intelligence and emerging technologies, developing solutions for the biggest challenges that government will face tomorrow. We are proud to employ and support military veterans who bring mission-first mindset, technical expertise, and leadership qualities that strengthen our work. Veterans, transitioning service members, and military spouses are strongly encouraged to apply.       Job Description:  A Senior Data Architect Engineer to support the DIA O&I Enterprise Integration and Assessments Data Team, focused on accelerating dataset understanding and delivering fit-for-purpose machine learning and data solutions. The role will help the team move from “what data do we have and what can it support?” to “what model or workflow should we implement, and how do we sustain it?” The emphasis is on selecting the right method or tool for the mission problem, including determining when ML is appropriate and when rules, heuristics, or simpler analytics are better suited Basic Requirements:   B.S. in related field with 5+ years of experience or 10+ years with no B.S. Must be a U.S. Citizen Must have an ACTIVE TS/SCI w/ CI Poly Clearance Senior-level experience supporting machine learning, data science, data architecture, or data engineering efforts Strong Python and SQL experience Experience with common machine learning frameworks and libraries, such as scikit-learn, PyTorch, or TensorFlow Experience assessing datasets for ML readiness, including data quality, metadata, labeling, ground truth, feature engineering, and constraints Experience designing, building, evaluating, and improving ML models based on mission or business use cases Experience defining model evaluation metrics and conducting error analysis Experience building reproducible technical workflows and documenting implementation approaches Experience using AWS SageMaker for experimentation, training, processing, pipelines, model registry, or deployment approaches Sound software engineering practices, including Git, readable and modular code, basic testing, documentation, and reproducibility Ability to work independently, provide technical recommendations, and interface with Government leads and senior stakeholders with limited oversight Familiarity with scalable data processing tools such as Spark or PySpark is a plus Responsibilities:  Independently assess and explain datasets, including content, structure, quality, gaps, lineage, metadata, constraints, and known limitations Identify what is needed to make datasets ML-ready, including labeling, ground truth, feature creation, and data quality considerations Recommend practical approaches for addressing risk considerations such as bias, drift, and model limitations Design, build, and iterate ML models appropriate to the data and use case, such as classification, entity or record matching, anomaly detection, and natural language processing, as applicable Establish baselines and define evaluation metrics tied to operational utility Perform error analysis to guide model improvements and inform recommendations Build reproducible workflows that can be rerun and sustained within the customer’s operating environment and security constraints Support implementation decisions, including batch versus real-time processing, resource and cost tradeoffs, and latency or throughput considerations Use AWS SageMaker for experimentation and execution, including notebo