Data Science Engineer Jobs in Maryland
Data Science Engineer jobs in Maryland are concentrated in the Baltimore-Washington corridor, Bethesda, and the National Capital Region suburbs, where federal agencies, defense contractors, and biotech firms generate consistent demand for professionals who can build and deploy machine learning systems, design data pipelines, and extract actionable intelligence from complex datasets. Employers with deep roots in Maryland's market include Northrop Grumman, Leidos, and Johns Hopkins University and Health System, which collectively sustain openings at junior, mid-level, and senior levels year-round. The most sought-after specialties include MLOps, natural language processing, and large-scale data infrastructure engineering. See the openings below and apply to the ones that match your experience.
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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.
Requirements: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
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.
Requirements: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
See All 21 Data Science Engineer Jobs in Maryland
Find roles in Maryland that match your experience and apply in just a few clicks.
Find Data Science Engineer JobsData Science Engineer Jobs by City in Maryland
Where Maryland roles are concentrated, by current openings.
Data Science Engineer Job Market in Maryland
A snapshot from current Maryland openings, updated as new roles post.
Who's Hiring
- Johns Hopkins University4
- Blend1
- GRVTY1G
- Perdue1
- Blend3601
Top Industries Hiring
- Education6
- Government & Public Sector2
- Consulting & Professional Services2
- Food & Beverage1
- Science & Research1
What Maryland Employers Look For
The qualifications that appear most often in data science engineer jobs across Maryland.
- Bachelor's or master's degree in computer science, statistics, or a related quantitative field
- Proficiency in Python and SQL for data manipulation, modeling, and pipeline development
- Experience designing and deploying machine learning models in production environments
- Familiarity with cloud platforms such as AWS, Azure, or Google Cloud for data engineering
- Strong command of big data frameworks including Spark, Hadoop, or similar distributed systems
- Active or obtainable security clearance preferred for Maryland defense and federal contracting roles
Data Science Engineer Jobs in Maryland: Frequently Asked Questions
How do you become a data science engineer in Maryland?
There is no state-issued license required to work as a data science engineer in Maryland, so the path runs through education and demonstrated technical skills. Most Maryland employers expect at least a bachelor's degree in computer science, mathematics, or statistics, and many federal contracting roles in the state require or strongly prefer a master's. Building a portfolio of deployed models and contributing to public repositories strengthens applications considerably. Candidates targeting defense or intelligence roles should pursue a federal security clearance early, as it opens the largest share of Maryland openings.
How much do data science engineers make in Maryland?
Data science engineers in Maryland earn a median of about $136,370 a year, based on May 2025 Bureau of Labor Statistics wage data, ranging from around $77,930 for the lowest 10% to over $213,270 for the top 10%. Pay rises with experience, specialty, and employer.
Which companies hire data science engineers in Maryland?
Employers hiring data science engineers in Maryland right now include Johns Hopkins University, Blend, and GRVTY, based on current listings on Migrate Mate as of July 2026. Maryland's concentration of federal agencies, defense primes, and research universities means a significant portion of these openings sit within organizations that hold government contracts and value domain expertise in security, healthcare, or scientific research.
Which Maryland cities have the most data science engineer jobs?
Laurel, Baltimore, and Columbia account for the largest share of data science engineer openings in Maryland, reflecting the state's core employment geography. The Baltimore-Bethesda-Rockville axis drives most demand, anchored by federal health agencies like the National Institutes of Health and the Centers for Medicare and Medicaid Services, major defense contractors headquartered along the I-270 corridor, and a dense cluster of biotech and cybersecurity firms that rely on advanced data engineering talent.
Are there remote data science engineer jobs in Maryland?
Yes, and more than most fields, because data science engineering is fundamentally desk-based, code-driven work that travels well across distributed teams. About 50% of data science engineer openings tied to Maryland are remote or hybrid as of July 2026, though classified government and defense contract roles typically require on-site presence at secured facilities. The most consistently remote positions tend to be in commercial analytics, data platform engineering, and ML infrastructure roles outside the federal sector.
How can I get hired as a data science engineer in Maryland with little or no experience?
The most realistic entry path is a junior data engineer or associate data scientist role at one of Maryland's large research institutions or federal contractors, where structured onboarding and mentorship are common. Johns Hopkins and the University of Maryland both hire research data associates who build toward full engineering roles, while organizations like Leidos and SAIC run intern-to-hire pipelines that absorb recent graduates. Candidates from adjacent roles such as business analyst, database administrator, or software developer can transition by demonstrating Python proficiency and completing a portfolio project involving end-to-end model deployment.
Where can I find and apply to data science engineer jobs in Maryland?
You can find and apply to data science engineer jobs in Maryland on Migrate Mate, which lists current Maryland openings updated regularly. Search for roles that fit your experience level, specialty, and preferred location within the state, then apply directly to the positions that match. No sign-up is required to view openings.
See All 21 Data Science Engineer Jobs in Maryland
Find roles in Maryland that match your experience and apply in just a few clicks.
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