Data Science Manager Jobs in Maryland

Position Overview

Diaconia is seeking a mid-level Data Science & AI/PQC Engineer to design and deliver AI-enabled cybersecurity and post-quantum cryptography (PQC) capabilities for federal mission customers. This role blends applied machine learning, data engineering, cloud-native software delivery, and cryptographic modernization to help agencies identify cryptographic assets, score quantum and cyber risk, monitor compliance, and transition legacy environments toward quantum-resilient architectures. The engineer will contribute to mission-facing prototypes, secure deployments, technical documentation, and stakeholder demonstrations in support of federal cyber modernization efforts.

Key Responsibilities

• Develop AI-driven PQC readiness capabilities that support cryptographic asset inventory, key-management mapping, legacy-system dependency analysis, automated risk scoring, and compliance monitoring for federal networks
• Integrate cybersecurity and infrastructure data from network scans, SIEM/security telemetry, vulnerability tools, configuration repositories, cryptographic discovery outputs, and mission systems into analytics-ready datasets
• Engineer cloud-native prototypes using Python, APIs, Docker, Kubernetes/Helm, CI/CD, and AWS or Azure government cloud environments to move analytics from proof-of-concept into secure, repeatable deployments
• Evaluate AI/ML effectiveness using mission-relevant metrics such as detection accuracy, false-positive rates, coverage, latency, response time, model drift, and remediation prioritization value
• Apply AI/ML techniques to structured and unstructured federal datasets, including network telemetry, vulnerability findings, cryptographic inventories, logs, NLP, time-series forecasting, anomaly detection, and classification models
• Develop and iterate on data pipelines to ingest, clean, transform, and analyze large-scale government datasets, such as network logs, cryptographic asset inventories, vulnerability scans, procurement data, case management records, sensor feeds, and supply chain data
• Prototype and evaluate large language model (LLM) applications including retrieval-augmented generation (RAG), prompt engineering, agentic workflows, and analyst-assist capabilities tailored to cyber, compliance, and mission assurance use cases
• Translate mission requirements from federal agency stakeholders into technical problem statements, data-driven solution approaches, backlog items, model evaluation plans, and implementation roadmaps
• Build dashboards and data visualizations to communicate threat trends, cryptographic risk, migration priority, model performance, compliance status, and analytical findings to both technical and non-technical government audiences
• Support responsible AI practices by contributing to model documentation, test plans, explainability artifacts, bias and performance assessments, and governance workflows aligned to applicable federal AI guidance (e.g., OMB M-25-21, OMB M-25-22, EO 14179, NIST AI RMF)
• Collaborate in agile teams by participating in sprint planning, demos, retrospectives, code reviews, experiment reviews, and technical documentation for secure federal delivery
• Present findings to internal teams and, where appropriate, to federal agency stakeholders through demos, briefings, white papers, remediation roadmaps, and architecture tradeoff discussions

Disclaimer "The responsibilities and duties outlined in this job description are intended to describe the general nature and level of work performed by employees within this role. However, they are not exhaustive and may be subject to change or modification at any time to meet the evolving needs of the organization.

Required Qualifications

• 3+ years of professional experience in data science, machine learning engineering, software engineering, cybersecurity analytics, cryptography modernization, or related applied technology delivery
• Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Cybersecurity, Information Systems, or a related technical field; additional relevant experience may substitute for degree requirements
• Proficiency with Python and SQL and experience building data pipelines, analytical workflows, APIs, dashboards, or production-grade AI/ML applications
• Working knowledge of cybersecurity and cryptographic concepts such as TLS, PKI, key management, encryption algorithms, vulnerability assessment, secure communications, and risk remediation
• Experience with cloud or containerized delivery using tools such as AWS, Azure, Docker, Kubernetes, Git, CI/CD pipelines, and Linux-based development environments
• U.S. citizenship and ability to obtain and maintain a U.S. government security clearance; active Secret, Top Secret, or TS/SCI clearance may be required by program
• Strong analytical thinking and ability to frame ambiguous problems into tractable analytical approaches
• Excellent written and verbal communication skills; ability to explain technical concepts to non-technical stakeholders

Preferred Qualifications

• Hands-on experience with post-quantum cryptography, crypto-agility, cryptographic discovery, PQC migration planning, or implementation of NIST PQC standards such as ML-KEM, ML-DSA, and SLH-DSA
• Experience building AI-enabled cybersecurity capabilities, including threat detection, anomaly detection, automated risk scoring, compliance monitoring, SIEM/log analytics, analyst-assist workflows, or cyber operations automation
• Experience deploying AI/ML or software capabilities into secure federal environments, such as DoD, IC, CUI, FedRAMP, CMMC, RMF, Zero Trust, CAC-enabled, air-gapped, or otherwise constrained mission settings
• Familiarity with secure communications and infrastructure modernization, including PKI, identity systems, key management, cloud security, encryption modernization, and legacy-system interoperability
• Experience with deep learning frameworks (PyTorch, TensorFlow, Hugging Face Transformers) and classical ML libraries (scikit-learn, XGBoost, pandas) used in applied analytics delivery
• Hands-on exposure to LLMs and generative AI applications including prompt engineering, fine-tuning, RAG pipelines, vector stores, model evaluation, and agentic frameworks such as LangChain, LangGraph, Semantic Kernel, or AutoGen
• Familiarity with cloud platforms (AWS GovCloud, Azure Government, or Google Cloud) and MLOps tooling such as MLflow, SageMaker, Vertex AI, Airflow, Kubeflow, or Databricks workflows
• Experience with data visualization tools (Tableau, Power BI, Plotly Dash, Kibana, Grafana, or similar) for executive dashboards, analyst workflows, and operational monitoring
• Knowledge of federal or mission data sources including agency-specific systems, network/security telemetry, vulnerability management platforms, USASpending, Data.gov, Census Bureau APIs, or operational mission repositories
• Prior professional, research, or project experience in a government, defense, intelligence, cybersecurity, public sector, or regulated commercial environment
• Coursework, projects, or applied experience in AI governance, responsible AI, trustworthy AI, model risk management, privacy, cybersecurity policy, or federal technology acquisition

What You'll Gain

• Quantum-resilient mission modernization: Build AI-enabled capabilities that help federal agencies understand cryptographic exposure, prioritize PQC migration, and improve mission assurance against emerging quantum-enabled cyber threats
• End-to-end technical ownership: Contribute across prototype design, data ingestion, ML experimentation, cloud deployment, stakeholder demonstrations, and transition planning for operational environments
• Mission-driven impact: Your work will directly support federal agencies tackling challenges in AI-driven cybersecurity, PQC readiness, cryptographic compliance, mission assurance, supply chain resilience, fraud detection, workforce analytics, and more
• Technical depth: Hands-on experience applying AI/ML, LLM, MLOps, cloud engineering, data engineering, and PQC methods to complex, real-world federal datasets - not toy problems
• Federal domain expertise: Exposure to the federal acquisition, compliance, cyber modernization, SBIR transition, and program environment that shapes how AI and PQC capabilities are deployed in government
• Mentorship: Work with senior data scientists, ML engineers, cybersecurity architects, and cryptography specialists who provide technical guidance and career coaching throughout the role
• Professional development: Access to internal learning resources, technical communities, industry certifications (AWS, Azure, Google Cloud, security, data, and AI), and speaker series