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Mid Level Bayesian Statistics Jobs in Virginia (NOW HIRING)

ERG

Mid-Level Water and Wastewater Engineer

Fairfax, VA

$85K - $120K/yr

Statistical software packages (e.g., R) and coursework in statistics $85,000 - $120,000 a year ERG offers competitive salaries and excellent benefits, including health and dental insurance, life ...

ERG

Mid-Level Water and Wastewater Engineer

Arlington, VA · On-site +1

$85K - $120K/yr

Statistical software packages (e.g., R) and coursework in statistics $85,000 - $120,000 a year ERG offers competitive salaries and excellent benefits, including health and dental insurance, life ...

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All JobsMid Level Bayesian Statistics JobsMid Level Bayesian Statistics Jobs in Virginia

Mid Level Bayesian Statistics information

What can you do with Bayesian statistics?

A mid-level Bayesian statistics professional can apply Bayesian methods to model uncertainty, update probabilities with new data, and improve decision-making processes across various fields such as healthcare, finance, and technology. Proficiency in statistical software like R or Python and understanding of probabilistic programming are essential for implementing these techniques effectively.

Is Bayesian statistics difficult?

Bayesian statistics as a mid-level role involves understanding probability models, prior and posterior distributions, and using tools like R or Python for analysis. While it requires a solid grasp of statistical concepts and programming skills, the difficulty depends on prior experience with mathematics and data analysis. Consistent practice and formal training can help develop proficiency in this specialized area.

What job should I do if I like statistics?

A mid-level Bayesian statistician typically works in data analysis, research, or modeling roles across industries such as healthcare, finance, or technology. These roles involve applying statistical methods, programming skills in languages like R or Python, and understanding Bayesian inference to solve complex problems. Certification or advanced degrees in statistics or related fields can enhance job prospects.

What is the difference between Mid Level Bayesian Statistics vs Data Scientist?

AspectMid Level Bayesian StatisticsData Scientist
Required CredentialsMaster's or PhD in Statistics, Mathematics, or related fieldBachelor's or higher in Data Science, Computer Science, or related field
Work EnvironmentResearch-focused, analytical, often in finance, healthcare, or academiaCross-functional teams, data analysis, machine learning, business insights
Industry UsageStatistical modeling, probabilistic analysis, research projectsData analysis, predictive modeling, data visualization

Mid Level Bayesian Statistics specialists focus on advanced probabilistic modeling and statistical inference, often in research or specialized industries. Data Scientists have a broader scope, combining statistical analysis with programming and machine learning to solve business problems. While both roles require strong analytical skills, Bayesian statisticians typically emphasize probabilistic models, whereas Data Scientists integrate multiple techniques for data-driven decision-making.

What type of statistician makes the most money?

Senior-level statisticians, such as data science managers or quantitative research directors, tend to earn the highest salaries. Specializations in machine learning, Bayesian methods, or roles in finance and technology often command higher compensation, especially with advanced skills and relevant certifications.
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Mid Level Bayesian Statistics jobs near you

Data Engineer - Classical Statistics & Machine Learning

BLN24

Mclean, VA • On-site

Full-time

Medical, Dental, Vision

Posted 15 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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