Data Scientist / Machine Learning Engineer

Posted 3 Days Ago
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Arlington, VA, USA
In-Office
160K-185K Annually
Senior level
Artificial Intelligence • Cloud • Information Technology • Security • Software
The Role
Develops and deploys predictive analytics, machine learning, and deep learning solutions for production use. Responsibilities include statistical analysis, feature engineering, model development, anomaly detection, NLP, computer vision, MLOps, CI/CD pipelines, cloud AI platform integration, data engineering collaboration, model monitoring, governance, and stakeholder communication. The role also supports large-scale analytics, AI modernization, and human-reviewed multi-source summarization.
Summary Generated by Built In
Job Summary & Responsibilities

Everforth ECS is seeking a Data Scientist/Machine Learning Engineer to join our team in Arlington, VA (Hybrid).  This position is contingent upon award.


We are seeking a talented Data Scientist / Machine Learning Engineer to design, develop, deploy, and optimize advanced analytics and machine learning solutions that drive business insights and operational efficiencies. This role combines data science, machine learning engineering, and software development to transform complex data into scalable, production-ready AI and predictive analytics solutions.


The ideal candidate will possess expertise in statistical analysis, machine learning algorithms, AI/ML-assisted clustering, feature engineering, model deployment, anomaly detection, and cloud-based AI platforms while collaborating closely with business stakeholders, data engineers, and technology teams.


Key Responsibilities

Data Science & Advanced Analytics

  • Analyze structured and unstructured data to identify trends, patterns, and actionable insights.
  • Develop predictive, prescriptive, and classification models to support business objectives.
  • Perform exploratory data analysis (EDA), feature engineering, and statistical modeling.
  • Design experiments and evaluate model performance using appropriate statistical methodologies.
  • Present findings and recommendations to technical and non-technical stakeholders.
  • Support efforts in anomaly detection.

Machine Learning Development

  • Design, build, train, and optimize machine learning and deep learning models.
  • Develop solutions for forecasting, anomaly detection, natural language processing (NLP), recommendation systems, and computer vision applications.
  • Evaluate and select appropriate algorithms based on business requirements and performance objectives.
  • Continuously improve model accuracy, scalability, and maintainability.

MLOps & Production Engineering

  • Deploy machine learning models into production environments.
  • Build automated model training, validation, deployment, and monitoring pipelines.
  • Implement CI/CD practices for machine learning workflows.
  • Support AI/ML-assisted clustering efforts.
  • Monitor model performance and address model drift, data drift, and operational issues.
  • Maintain model governance, versioning, and documentation standards.

Data Engineering & Platform Integration

  • Collaborate with data engineers to develop scalable data pipelines and feature stores.
  • Integrate machine learning solutions into enterprise applications and business processes.
  • Optimize data processing workflows for large-scale datasets.
  • Ensure data quality, security, and compliance standards are maintained.

Cloud & AI Platforms

  • Develop and deploy solutions using cloud-native AI and machine learning services.
  • Leverage platforms such as Azure Machine Learning, AWS SageMaker, Databricks, Vertex AI, or equivalent technologies.
  • Perform multi-source summarization with human-review workflow by combining AI-driven aggregation of diverse sources with targeted human validation.
  • Utilize distributed computing frameworks to support large-scale analytics workloads.
  • Support enterprise AI strategy and modernization initiatives.

Collaboration & Innovation

  • Partner with business leaders to identify opportunities for AI and advanced analytics solutions.
  • Translate business requirements into machine learning use cases and technical requirements.
  • Stay current on emerging technologies, AI trends, and industry best practices.
  • Contribute to innovation initiatives, proofs of concept, and research activities.

Salary Range: $160,000 - $185,000

General Description of Benefits 


Preferred Qualifications
  • Top Secret Clearance
  • Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
  • 5+ years of experience in Data Science, Machine Learning Engineering, Artificial Intelligence, or Advanced Analytics.
  • Strong understanding of machine learning algorithms, statistical analysis, and data modeling techniques.
  • Experience building and deploying machine learning models in production environments.
  • Proficiency in Python and machine learning libraries/frameworks.
  • Strong knowledge of SQL and data manipulation techniques.
  • Experience working with large datasets and cloud-based data platforms.
  • Excellent problem-solving, analytical, and communication skills.

Skills Required

  • Top Secret clearance
  • Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field
  • 5+ years of experience in Data Science, Machine Learning Engineering, Artificial Intelligence, or Advanced Analytics
  • Strong understanding of machine learning algorithms, statistical analysis, and data modeling techniques
  • Experience building and deploying machine learning models in production environments
  • Proficiency in Python and machine learning libraries or frameworks
  • Strong knowledge of SQL and data manipulation techniques
  • Experience working with large datasets and cloud-based data platforms
  • Excellent problem-solving, analytical, and communication skills

ECS Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about ECS and has not been reviewed or approved by ECS.

  • Healthcare Strength ECS advertises multiple national-network medical plan options with HSA eligibility alongside dental and vision coverage. Coverage generally begins quickly and is paired with company-paid short- and long-term disability, adding stability to the health package.
  • Retirement Support A 401(k) with Safe Harbor and immediate vesting on employer contributions is emphasized, with an employer match available. Access to an employee stock purchase plan via the parent company provides an additional savings avenue.
  • Parental & Family Support Paid parental leave up to 30 days, adoption assistance, and other family-oriented leaves are highlighted. Feedback suggests these offerings add meaningful value beyond base pay for many roles.

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The Company
HQ: Elkhorn, NE
2,129 Employees
Year Founded: 1993

What We Do

ECS, a segment of ASGN (NYSE: ASGN), delivers advanced solutions and services in cloud, cybersecurity, artificial intelligence (AI), machine learning (ML), application and IT modernization, and science and engineering. The company solves critical, complex challenges for customers across the U.S. public sector, defense, intelligence and commercial industries. ECS maintains partnerships with leading cloud, cybersecurity, and AI/ML providers and holds specialized certifications in their technologies. Headquartered in Fairfax, Virginia, ECS has more than 3,400 employees throughout the U.S. and has been recognized as a Top Workplace by The Washington Post for the last five years.

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