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Statistical Engineering Jobs in Virginia (NOW HIRING)

ECS

Data Scientist

Springfield, VA · On-site

A Bachelor's Degree in Data Science, Math, Finance, Statistics, Information Management, Computer Science, Engineering, Economics or an equivalent field * 5+ years of working experience in one of the ...

NAVA Software Solutions

Data Scientist

Merrifield, VA · On-site

Understanding of data models, large datasets, business/technical requirements, BI tools, statistical programming languages and libraries * Familiar with Data Engineering concepts * Familiar with the ...

RecruitTalent LLC.

Data Scientist

Fredericksburg, VA · On-site

$60 - $70/hr

Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Engineering, or related discipline. * Experience: * 5-8 years of experience in data analytics, machine learning, or AI ...

Beyond SOF

Data Analyst (TS/SCI, 3 yrs exp)

Reston, VA · On-site

Knowing a statistical programming language like Python to enable handling large sets of data and performing complex equations. * Data visualization: Capable of consolidating findings in a clear ...

Elder Research

Senior Data Engineer

Arlington, VA · Hybrid

$122K - $165K/yr

Bachelor of Science degree in a relevant field such as statistics, computer science, economics, mathematics, analytics, data science, data engineering, business, or social sciences * 8+ years of ...

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All JobsStatistical Engineering JobsStatistical Engineering Jobs in Virginia

Statistical Engineering information

What are the key skills and qualifications needed to thrive as a Statistical Engineer, and why are they important?

To thrive as a Statistical Engineer, you need strong quantitative analysis skills, a background in statistics or mathematics, and often a relevant degree such as in engineering or applied statistics. Proficiency with statistical software (e.g., R, SAS, Python), data management systems, and sometimes Six Sigma certification is typically required. Critical thinking, problem-solving, and clear communication are crucial soft skills for interpreting data and collaborating with multidisciplinary teams. These skills ensure accurate data-driven decisions, efficient process improvements, and effective solutions to complex engineering challenges.

What is the difference between Statistical Engineering vs Data Scientist?

AspectStatistical EngineeringData Scientist
Required credentialsStatistics, Data Analysis, EngineeringStatistics, Computer Science, Data Analysis
Work environmentManufacturing, R&D, Engineering teamsBusiness, Tech, Research sectors
Employer usageOptimizing processes, designing experimentsBuilding models, insights, predictive analytics

Statistical Engineering focuses on applying statistical methods to improve engineering processes and product development, often within manufacturing or R&D settings. Data Scientists analyze large datasets to extract insights, build predictive models, and support business decisions. While both roles require strong statistical skills, Statistical Engineering emphasizes process optimization and experimental design, whereas Data Scientists focus on data-driven insights across diverse industries.

How does a Statistical Engineer typically collaborate with cross-functional teams to implement data-driven solutions?

Statistical Engineers frequently work alongside data scientists, software engineers, and business analysts to design and implement robust data-driven solutions. They are responsible for translating complex statistical models into actionable insights and ensuring that these models are integrated effectively within existing systems. Collaboration often involves regular meetings to align on project goals, sharing progress updates, and troubleshooting technical challenges together. This interdisciplinary teamwork is essential for ensuring that statistical methodologies are not only theoretically sound but also practically applicable to real-world business problems.

What is statistical engineering?

Statistical engineering is an interdisciplinary field that focuses on the integration and application of statistical methods and principles to solve complex, large-scale problems in science, business, and engineering. It involves designing data collection processes, analyzing and interpreting data, and implementing statistical solutions within larger systems. Statistical engineers often work on projects that require collaboration with other engineering disciplines, using statistics as a foundational tool to drive decision-making and innovation.
What are popular job titles related to Statistical Engineering jobs in Virginia? For Statistical Engineering jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Statistical Engineering jobs in Virginia look for? The top searched job categories for Statistical Engineering jobs in Virginia are:
What cities in Virginia are hiring for Statistical Engineering jobs? Cities in Virginia with the most Statistical Engineering job openings:
Statistical Engineering jobs near you

Data Engineer - Classical Statistics & Machine Learning

BLN24

Mclean, VA • On-site

Full-time

Medical, Dental, Vision

Posted 14 days ago

Job description

Job Title: Data Engineer
Company: BLN24
About Us: We find strength in teamwork-a better you is a better us
BLN24 is an award-winning Management Consulting Firm that supports the U.S. Federal Government in successfully achieving their mission and goals. Our service and solutions delivery start with understanding each client's end-state, and then seamlessly integrating within each Agency's organization to improve and enhance strategic and technical operations and deployments.
Position Overview:
BLN24 is seeking a mid-level Data Engineer to support a large-scale data and analytics platform modernization effort for a federal statistical agency client. This is a hybrid role: data engineering (building and maintaining the pipelines that bring data into the platform) and applied data science (using classical statistics and machine learning to analyze that data once it's available).
The ideal candidate is equally comfortable writing production-grade ingestion and
transformation code as they are designing and validating a statistical or ML model.
This role works closely with SMEs across multiple program areas to understand source data, build reliable ETL/ingestion pipelines, and apply analytical methods - anomaly detection, statistical modeling, and machine learning - to support operational decision-making.
Key Responsibilities:
Data Engineering
  • Design, build, and maintain ETL/ELT pipelines to ingest data from multiple source systems into the platform's central data store
  • Develop and maintain data ingestion workflows for both batch and near-real-time sources
  • Implement data validation, cleaning, and transformation logic to ensure data quality and consistency across pipelines
  • Work within a modern lakehouse/cloud data architecture, optimizing pipeline performance and reliability
  • Build and maintain data models and schemas that support downstream analytics and reporting needs
  • Monitor pipeline health, troubleshoot failures, and implement logging/alerting for data quality issues
  • Document data lineage, transformation logic, and pipeline architecture for governance and reproducibility

Data Science / Statistics & ML
  • Apply classical statistical methods (hypothesis testing, regression, time-series analysis, distributional comparisons) to identify trends, anomalies, and outliers in operational data
  • Design and implement benchmarking approaches that compare production data against historical, modeled, or external reference values
  • Develop and evaluate machine learning models where appropriate, balancing predictive performance with interpretability for non-technical stakeholders
  • Investigate flagged anomalies by digging into underlying data to identify root causes and contributing factors
  • Work with SMEs to translate operational questions into analytical approaches, and clearly communicate statistical/ML findings and their limitations
  • Account for data sensitivity classifications and governance requirements when designing analyses and models
  • Collaborate with visualization-focused team members to ensure outputs of statistical/ML work are presented clearly to stakeholders

Required Qualifications:
  • Bachelor's degree in Data Science, Statistics, Computer Science, Engineering, or related field (or equivalent experience)
  • 3-5 years of experience spanning both data engineering and data science/statistical analysis
  • Strong proficiency in Python, including experience with data engineering libraries (e.g., pandas, PySpark) and statistical/ML libraries (e.g., scikit-learn, statsmodels)
  • Hands-on experience building and maintaining ETL/ELT pipelines, including ingestion, transformation, and validation logic
  • Solid grounding in classical statistical methods (hypothesis testing, regression, distributional analysis) and practical machine learning techniques
  • Experience working with SQL and relational/distributed data systems
  • Ability to work within a federal data environment, including familiarity with data sensitivity tiers and access/disclosure constraints
  • Strong communication skills, with the ability to explain technical/statistical concepts to non-technical stakeholders

Preferred Qualifications:
  • Prior experience supporting federal statistical agencies or other federal data programs
  • Familiarity with Databricks or modern lakehouse architectures (Spark, Delta Lake, etc.)
  • Experience with workflow orchestration tools (e.g., Airflow, Databricks Workflows)
  • Experience designing anomaly-detection or outlier-detection approaches beyond standard threshold-based methods
  • Exposure to disclosure avoidance concepts or working with regulated/protected government data
  • Experience working across multiple coding environments (Python, R, SAS) within the same analytics platform
  • Background in requirements gathering or systems design for enterprise data platforms

Work Environment:
  • Contract position supporting a federal agency data modernization engagement
  • Collaborative, cross-functional environment working alongside data engineers, data scientists, architects, and program SMEs
  • Requires U.S. citizenship and ability to obtain a public trust or other clearance/suitability determination typical of federal contractor engagements

What BLN24 brings to the Game:
BLN24 benefits are game changing. We like our team to play hard and that means they need to be taken care of - physically, financially, and emotionally. We make sure to keep them in the game by giving them access to generous medical, dental, and vision plans.
  • You can join one of the fastest growing companies headquartered in the Washington DC Metro Area. We give you the opportunity to work in different sectors, so you have the chance at variety while maintaining stability.
  • Flexibility at BLN24 allows each individual the opportunity to balance quality work and their personal lives. Depending on projects, we allow remote working opportunities so you can always be in the game no matter where you call home.
BLN24 is an Equal Opportunity Employer. We believe people are our strength and understand diverse talents are key to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. In accordance with applicable law, we make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as any mental health or physical disability needs.

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