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Data Science Machine Learning Jobs in Maryland (NOW HIRING)

Total Wine & More

Director, Data Science

Bethesda, MD · On-site

Responsibilities : • Spearhead the strategy and execution of enterprise-wide data science, machine learning, and AI initiatives, ensuring alignment with long-term organizational goals and business ...

New

WSSC Water

Data Scientist

Laurel, MD · On-site

Stays updated on the latest developments in data science, machine learning, and related technologies * Identifies and recommends opportunities for process improvement and innovation * Documents ...

iNovex Information Systems

Data Scientist 3

Annapolis, MD · On-site

$157K - $215K/yr

Job Brief Data Science, Machine Learning Are you VIGILANT about your career? RealmOne definitely is! RealmOne was built on the principle that people matter first and foremost. We believe in providing ...

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All JobsData Science Machine Learning JobsData Science Machine Learning Jobs in Maryland

Data Science Machine Learning information

See Maryland salary details

$36.4K

$119.1K

$190.7K

How much do data science machine learning jobs pay per year?

As of Jun 29, 2026, the average yearly pay for data science machine learning in Maryland is $119,123.00, according to ZipRecruiter salary data. Most workers in this role earn between $95,600.00 and $132,000.00 per year, depending on experience, location, and employer.

Which has more salary, CS or AI?

Data Science and Machine Learning roles in AI generally have higher salaries than traditional computer science positions due to specialized skills in deep learning, neural networks, and advanced algorithms. AI roles often require expertise in programming languages like Python and frameworks such as TensorFlow, which are highly valued in the job market. Salaries vary by experience, location, and industry, but AI-focused positions tend to offer higher compensation on average.

What are the key skills and qualifications needed to thrive as a Data Science Machine Learning professional, and why are they important?

To thrive as a Data Science Machine Learning professional, you need a strong background in statistics, programming (usually Python or R), and a solid understanding of machine learning algorithms, often supported by a degree in computer science, mathematics, or a related field. Familiarity with tools like TensorFlow, scikit-learn, SQL databases, and cloud platforms, as well as certifications such as AWS Certified Machine Learning, are typically valuable. Critical thinking, problem-solving, and effective communication are vital soft skills for interpreting data and collaborating with stakeholders. These skills enable professionals to develop robust models, extract actionable insights, and drive data-driven decision-making in organizations.

What engineers make $500,000?

Senior data science and machine learning engineers with extensive experience, advanced skills in programming, statistical analysis, and deep learning, and often working in high-demand industries or at large tech companies can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially at executive or specialized levels.

What are some common challenges faced when deploying machine learning models as a Data Science Machine Learning professional?

A frequent challenge in this role is bridging the gap between building accurate models in a controlled environment and deploying them effectively in production systems. Issues such as data drift, model performance degradation, and integration with existing IT infrastructure often arise. Collaboration with engineering and IT teams is crucial to ensure models are scalable, maintainable, and secure. Regular monitoring and updating of deployed models are also essential responsibilities to sustain their value to the business.

What is the difference between Data Science Machine Learning vs Data Analyst?

AspectData Science Machine LearningData Analyst
Required SkillsProgramming (Python, R), statistics, machine learning algorithmsData visualization, SQL, basic statistics
Work EnvironmentDeveloping models, coding, experimenting with algorithmsData reporting, dashboard creation, data cleaning
Industry UsageTech, finance, healthcare, where predictive models are neededBusiness intelligence, marketing, operations

Data Science Machine Learning professionals focus on building predictive models and algorithms using programming and advanced statistics, often working on complex projects. Data Analysts primarily interpret data through visualization and reporting to support business decisions. While both roles require data skills, Data Science Machine Learning involves more technical programming and modeling, whereas Data Analysts focus on data interpretation and presentation.

Do data scientists work with machine learning?

Data scientists often work with machine learning as a core part of their role, developing models to analyze data and make predictions. They use tools like Python, R, and libraries such as scikit-learn or TensorFlow to build and deploy machine learning algorithms. Knowledge of statistics, programming, and data manipulation is essential for this work.

What is data science machine learning?

Data science machine learning refers to the use of algorithms and statistical models to analyze and draw insights from complex data sets. In this field, professionals use machine learning techniques to build predictive models, automate decision-making processes, and uncover patterns in data. Machine learning is a core component of data science, enabling systems to improve their performance over time without being explicitly programmed. Data scientists with machine learning expertise are in high demand across industries like healthcare, finance, and technology.

Which 3 jobs will survive AI?

Data science and machine learning roles are expected to persist as they require complex problem-solving, domain expertise, and creativity that AI tools currently cannot fully replicate. Jobs involving strategic decision-making, ethical considerations, and interpersonal skills, such as data analysts, AI ethics specialists, and AI system trainers, are also likely to remain in demand. Continuous learning and proficiency with AI tools will be essential for these roles to adapt and thrive.
What cities in Maryland are hiring for Data Science Machine Learning jobs? Cities in Maryland with the most Data Science Machine Learning job openings:
Data Science Machine Learning jobs near you
Infographic showing various Data Science Machine Learning job openings in Maryland as of June 2026, with employment types broken down into 62% Full Time, 36% Part Time, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $119,123 per year, or $57.3 per hour.
Systems Architect - Data Science & Advanced Analytics

Systems Architect - Data Science & Advanced Analytics

Analytica

Bethesda, MD • On-site

$68 - $87.50/hr

Full-time

Posted 17 days ago

Key responsibilities

  • Lead the design and implementation of scalable data science environments and advanced analytics solutions.

  • Architect enterprise data architectures and data pipelines utilizing Databricks Lakehouse, Delta Lake, and Apache Spark.

  • Collaborate with cross-functional teams and business stakeholders to define analytics roadmaps and deliver high-impact analytical products.

Job description

Analytica is seeking a Systems Architect – Data Science & Advanced Analytics to provide strategic and technical leadership in designing, implementing, and modernizing enterprise data and advanced analytics solutions. This role serves as the lead architect for data science initiatives, responsible for developing scalable analytics environments, machine learning solutions, and enterprise data architectures with a strong emphasis on Databricks, Apache Spark, and modern cloud-based data platforms.
The ideal candidate will work closely with data scientists, data engineers, business stakeholders, and technical leadership to develop innovative analytics solutions that transform complex data into actionable insights. This individual will provide technical vision, establish best practices, and lead the adoption of advanced analytics and machine learning capabilities across the organization.
Key Responsibilities
Data Science Leadership

  • Serve as the Lead Architect for enterprise Data Science and Advanced Analytics initiatives.
  • Develop and drive the organization's vision and strategy for Big Data, Artificial Intelligence, and Machine Learning solutions.
  • Lead the design and implementation of scalable data science environments that support experimentation, model development, validation, and deployment.
  • Provide technical leadership and mentorship to Data Scientists, Machine Learning Engineers, and Analytics teams.
  • Identify emerging technologies and analytical methodologies that improve organizational capabilities and mission outcomes.
Advanced Analytics & Machine Learning
  • Architect end-to-end machine learning and advanced analytics solutions using modern data platforms and distributed computing frameworks.
  • Design scalable workflows for data preparation, feature engineering, model training, evaluation, and production deployment.
  • Collaborate with cross-functional teams to translate business and mission objectives into advanced analytical solutions.
  • Guide the development of predictive models, statistical analyses, optimization models, and AI-driven decision support tools.
  • Establish best practices for model governance, reproducibility, documentation, and performance monitoring.
Data Architecture & Big Data Solutions
  • Design enterprise data architectures that enable large-scale analytics and machine learning workloads.
  • Lead the development of data pipelines and analytical frameworks utilizing Databricks Lakehouse architecture, Delta Lake, and Apache Spark.
  • Ensure data solutions are scalable, reliable, high-performing, and aligned with enterprise architecture standards.
  • Architect integrated data ecosystems that support structured, semi-structured, and unstructured data sources.
  • Lead modernization initiatives that enhance data accessibility, analytical capabilities, and operational efficiency.
Strategic Collaboration
  • Partner with executive leadership and business stakeholders to define data and analytics roadmaps.
  • Translate complex technical concepts into business-focused recommendations and strategic initiatives.
  • Lead architecture reviews and provide expert guidance on enterprise analytics solutions.
  • Foster collaboration between Data Science, Data Engineering, and business teams to deliver high-impact analytical products.

Required Qualifications
Experience
  • Bachelor's degree in Computer Science, Data Science, Statistics, Engineering, Mathematics, or a related technical discipline (Master's preferred).
  • 10+ years of progressive experience designing and implementing advanced analytics, machine learning, or Big Data solutions.
  • Demonstrated experience serving as a technical lead or subject matter expert for enterprise data science initiatives.
  • Experience leading technical teams and architecting large-scale analytics platforms.
  • Experience architecting enterprise-scale data science and advanced analytics platforms.
  • Experience developing AI/ML solutions supporting mission-critical or large-scale business operations.
  • Knowledge of modern data governance and responsible AI principles.
  • Experience with experimentation frameworks, model operationalization, and analytical product development.
  • Familiarity with Agile product development and collaborative data science workflows.
Required Technical Skills
Data Science & Machine Learning
  • Machine Learning model development and lifecycle management
  • Statistical analysis and predictive modeling
  • Artificial Intelligence and Advanced Analytics methodologies
  • Feature engineering and model evaluation techniques
  • Data exploration and analytical solution design
Big Data & Data Engineering
  • Databricks Lakehouse Platform
  • Apache Spark
  • Delta Lake
  • Distributed data processing frameworks
  • ETL/ELT and modern data pipeline architectures
  • Data modeling and enterprise data architecture
Analytics & Visualization
  • Advanced analytics solution design
  • Business Intelligence and data visualization concepts
  • Dashboard and reporting architecture
  • Data storytelling and insight generation
Leadership
  • Technical strategy development
  • Enterprise architecture governance
  • Cross-functional team leadership
  • Stakeholder engagement and executive communication
  • Mentoring and coaching technical teams
Certifications:
Candidates should possess one or more of the following certifications:
Databricks
  • Databricks Certified Professional Data Scientist
  • Databricks Certified Professional Data Engineer
  • Databricks Certified Machine Learning Associate
  • Databricks Certified Developer for Apache Spark
Big Data & Analytics
  • Apache Spark or Big Data certifications
  • Tableau Certified Professional (Desktop or Server)
  • Other industry-recognized data science, analytics, or visualization certifications

Analytica LLC is an Equal Opportunity Employer. We are committed to providing equal employment opportunities to all individuals, regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or any other characteristic protected by applicable federal, state, or local law. As a federal contractor, we comply with the Vietnam Era Veterans' Readjustment Assistance Act (VEVRAA) and take affirmative action to employ and advance in employment qualified protected veterans. We ensure that all employment decisions are based on merit, qualifications, and business needs. We prohibit discrimination and harassment of any kind. Analytica LLC also provides reasonable accommodations to applicants and employees with disabilities, in accordance with applicable law.

To enhance efficiency, fairness, and accuracy, Analytica may use AI-assisted tools to support certain aspects of our hiring process.

  • Application Review: AI tools may help identify skills and experiences relevant to the role.
  • Interview Support: AI-powered notetaking tools may be used during interviews to document discussions and summarize key points.

These tools are used to assist our team. All hiring decisions are made by Analytica recruiters and hiring managers.

By submitting an application, you acknowledge that AI-assisted tools may be used to support parts of the application and interview process.

When receiving email communication from Analytica, please ensure that the email domain is analytica.net to verify its authenticity.

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