Job description

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 notebooks or Studio, training jobs, processing jobs, pipelines or automation, model registry, and deployment approaches as permitted Provide clear technical recommendations to the team and Government lead on data strategy, modeling choices, architecture patterns, and implementation plans Proactively identify technical risks, opportunities, and recommended paths forward with minimal oversight ACTIVE TS/SCI with CI POLY CLEARANCE REQUIRED* MUST BE U.S. CITIZEN Work Location: ON-SITE SUPPORT DIA HQ in Washington D.C. Why Tria? What defines the Tria brand is more than just our dedication to excellence in our craft; it’s our incredible team of dedicated, talented, and passionate people that make Tria so exceptional. As people powering possible, we are all partners in our team’s shared success. As a company that cares about people, we seek to cultivate a culture in which all can thrive personally and professionally. We offer a top-tier benefits package to invest in your physical, mental, and financial health and wellness so that you can be your best self - at work and in life. At Tria, we are growth-minded, entrepreneurial in spirit, and committed to fostering a culture of inclusion and opportunity for all. Whatever your background, your role, your department, or stage in your professional journey, here you will have opportunities to learn new skills, seize new challenges, and advance your career as we grow. California Consumer Privacy Act (CCPA) We are committed to protecting your privacy. As part of our compliance with the California Consumer Privacy Act (CCPA), we want to inform you about how we collect, use, and protect your personal information during the job application process. For more details, please review oag.ca.gov .

Requirements

Must-haves

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
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 notebooks or Studio, training jobs, processing jobs, pipelines or automation, model registry, and deployment approaches as permitted
Provide clear technical recommendations to the team and Government lead on data strategy, modeling choices, architecture patterns, and implementation plans

Nice-to-haves

Familiarity with scalable data processing tools such as Spark or PySpark is a plus

Application prep

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Keywords to mirror

machine learningmlpythonsqlaws

Platform: greenhouse
Effort: quick

Benefits

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