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Probabilistic Modeling Jobs in Washington, DC (NOW HIRING)

RIT Solutions, Inc.

AI Engineer

Vienna, VA · On-site

... modeling to drive insights and inform AI-driven solutions • Analyze graph-structured data to detect anomalies, extract probabilistic patterns, and support graph-based intelligence • Build NLP ...

Genex Systems

Hydraulic Research Engineer

Mclean, VA · On-site

CFD simulations and CAD model designs may be included to complement physical tests. Once assigned a ... Knowledge of hydrology, statistics, and probabilistic analysis. * Knowledge of HEC-18, HEC-23, and ...

Genex Systems

Hydraulic Research Engineer

Mclean, VA · On-site

CFD simulations and CAD model designs may be included to complement physical tests. Once assigned a ... Knowledge of hydrology, statistics, and probabilistic analysis. * Knowledge of HEC-18, HEC-23, and ...

Improbable LLC

Applied Scientist

Arlington, VA · On-site

Working along experts in AI, Machine Learning, computational modeling, and distributed systems, you ... Probabilistic programming. You can see our early open-sourced work here. * Working with real ...

Improbable LLC

Applied Scientist

Arlington, VA · On-site

Working along experts in AI, Machine Learning, computational modeling, and distributed systems, you ... Probabilistic programming. You can see our early open-sourced work here. * Working with real ...

Genex Systems

Hydraulic Research Engineer

Mclean, VA · On-site

CFD simulations and CAD model designs may be included to complement physical tests. Once assigned a ... Knowledge of hydrology, statistics, and probabilistic analysis. * Knowledge of HEC-18, HEC-23, and ...

Genex Systems

Hydraulic Research Engineer

Mclean, VA · On-site

CFD simulations and CAD model designs may be included to complement physical tests. Once assigned a ... Knowledge of hydrology, statistics, and probabilistic analysis. * Knowledge of HEC-18, HEC-23, and ...

Genex Systems

Hydraulic Research Engineer

Mclean, VA · On-site

CFD simulations and CAD model designs may be included to complement physical tests. Once assigned a ... Knowledge of hydrology, statistics, and probabilistic analysis. * Knowledge of HEC-18, HEC-23, and ...

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Probabilistic Modeling information

What is the difference between Probabilistic Modeling vs Data Scientist?

AspectProbabilistic ModelingData Scientist
Required CredentialsDegree in statistics, mathematics, or related fields; knowledge of probability theoryDegree in computer science, statistics, or related fields; programming skills
Work EnvironmentResearch-focused, often in analytics or data science teamsCross-functional teams, including business, engineering, and analytics
Industry UsageUsed in analytics, finance, healthcare, and research for modeling uncertaintyApplied across industries for data analysis, predictive modeling, and decision-making

Probabilistic Modeling focuses on developing models based on probability theory to understand uncertainty, while Data Scientists utilize a broader set of skills including programming, data analysis, and machine learning to extract insights from data. Both roles often overlap but serve different primary purposes within data-driven organizations.

What is probabilistic modeling?

Probabilistic modeling is a mathematical framework used to represent uncertain events or data by using probability distributions. Instead of giving a single outcome, it accounts for variability and randomness, allowing predictions and inferences even when information is incomplete or ambiguous. Probabilistic models are widely used in fields like statistics, machine learning, finance, and engineering to analyze data, make forecasts, and support decision-making under uncertainty.

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

To thrive as a Probabilistic Modeler, you need a strong background in mathematics, statistics, and probability theory, often supported by a degree in applied mathematics, statistics, or a related field. Proficiency with programming languages like Python or R, and experience with statistical modeling tools and software such as TensorFlow or PyMC, are typically required. Strong analytical thinking, problem-solving abilities, and effective communication skills help translate complex models into actionable insights. These skills are vital for designing accurate models, interpreting uncertainty, and supporting data-driven decisions across various industries.

What are some common challenges faced by professionals in probabilistic modeling roles, and how can they be managed?

Professionals in probabilistic modeling often encounter challenges such as working with incomplete or noisy data, choosing the right model complexity, and ensuring model interpretability for stakeholders. Managing these challenges involves strong statistical knowledge, regular collaboration with domain experts, and effective communication to translate complex results for non-technical team members. Staying up-to-date with the latest tools and methodologies, and participating in peer reviews, can also help maintain model accuracy and reliability.
What are popular job titles related to Probabilistic Modeling jobs in Washington, DC? For Probabilistic Modeling jobs in Washington, DC, the most frequently searched job titles are:
What job categories do people searching Probabilistic Modeling jobs in Washington, DC look for? The top searched job categories for Probabilistic Modeling jobs in Washington, DC are:
Probabilistic Modeling jobs near you

AI Engineer

RIT Solutions, Inc.

Vienna, VA • On-site

Full-time

Re-posted 2 days ago

Job description

Job Summary:
RIT Solutions, Inc. is seeking an AI Engineer to perform statistical analysis and develop AI-driven solutions. The role involves analyzing graph-structured data, building NLP pipelines, and collaborating with cross-functional teams to implement data-driven models and services.
Responsibilities:
• Perform statistical analysis, clustering, and probability modeling to drive insights and inform AI-driven solutions
• Analyze graph-structured data to detect anomalies, extract probabilistic patterns, and support graph-based intelligence
• Build NLP pipelines with a focus on NER, entity resolution, ontology extraction, and scoring
• Contribute to AI/ML engineering efforts by developing, testing, and deploying data-driven models and services
• Apply ML Ops fundamentals, including experiment tracking, metric monitoring, and reproducibility practices
• Collaborate with cross-functional teams to translate analytical findings into production-grade capabilities
• Prototype quickly, iterate efficiently, and help evolve data science best practices across the team
Qualifications:
Required:
• Solid experience in statistical modeling, clustering techniques, and probability-based analysis
• Hands-on expertise in graph data analysis, including anomaly detection and distribution pattern extraction
• Strong NLP skills with practical experience in NER, entity/ontology extraction, and related evaluation methods
• An engineering-forward mindset with the ability to build, deploy, and optimize real-world solutions (not purely theoretical)
• Working knowledge of ML Ops basics, including experiment tracking and key model metrics
• Proficiency in Python and common data science/AI libraries
• Strong communication skills and the ability to work collaboratively
Company:
Jobdiva Job Portal: https://www1.jobdiva.com/candidates/myjobs/searchjobsdone.jsp?a=xbjdnwgjodtga1y1im2g881fkkeiwd0775lbvq8yqgps8vb2q36w2vj1ga6xxork&compid=-1 Recruitment (contingency search and campus selection). Founded in 2019, the company is headquartered in Arlington, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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