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Statistical Modeling Jobs (NOW HIRING)

LexisNexis

Sr Statistical Modeler

$125K - $209K/yr

Applies and integrates statistical, mathematical, predictive modeling and business analysis skills to manage and manipulate complex data from a variety of sources * Develops and maintains ...

RELX Group plc

Sr Statistical Modeler

Manhattan, NY · On-site +1

$125K - $209K/yr

Applies and integrates statistical, mathematical, predictive modeling and business analysis skills to manage and manipulate complex data from a variety of sources * Develops and maintains ...

Stellent IT LLC

Sr. Statistician//W2

Bloomington, IN · On-site

$75K - $93K/yr

... statistical modeling, and related methods. This role may also contribute to the revision of policies, procedures, and training materials related to statistical practice. Sr. Statistician Hybrid job ...

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Statistical Modeling information

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$36.5K

$55.4K

$99K

How much do statistical modeling jobs pay per year?

As of Jul 2, 2026, the average yearly pay for statistical modeling in the United States is $55,350.00, according to ZipRecruiter salary data. Most workers in this role earn between $42,000.00 and $60,000.00 per year, depending on experience, location, and employer.

How much do data modelers make?

Data modelers, also known as statistical modelers, typically earn between $70,000 and $120,000 annually, depending on experience, education, and location. Senior roles or those with specialized skills in machine learning or programming may earn higher salaries, especially in tech hubs or large organizations.

Are statisticians highly paid?

Statisticians are generally well-paid, with median salaries often above the national average, especially for those with advanced skills in statistical software, programming, and data analysis. Salaries vary based on experience, education, industry, and location, but the profession is considered financially rewarding compared to many other roles in data analysis and modeling.

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

Professionals in statistical modeling often encounter challenges such as dealing with incomplete or messy data, selecting the most appropriate modeling techniques, and clearly communicating complex results to non-technical stakeholders. Managing these challenges typically involves collaborating closely with data engineers and domain experts, employing robust data cleaning practices, and staying up-to-date with new statistical methods. Additionally, effective communication skills are essential for translating technical findings into actionable business insights, ensuring that modeling efforts drive real-world impact.

What do statistical models do?

Statistical modeling involves creating mathematical representations of data to analyze relationships, make predictions, and inform decision-making. Professionals in this field use tools like regression analysis and statistical software to interpret complex data sets and support evidence-based conclusions.

What is statistical modeling?

Statistical modeling is the process of using mathematical models and statistical techniques to analyze data, identify patterns, and make predictions or inferences. It involves building models that represent relationships between variables in real-world systems. These models can be used for forecasting, hypothesis testing, and decision-making in various fields such as business, science, and engineering. Statistical modeling helps turn raw data into actionable insights by quantifying uncertainty and highlighting significant trends.

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

To excel as a Statistical Modeler, a solid background in statistics, mathematics, and data analysis—often supported by a degree in a quantitative field—is essential. Proficiency with statistical software such as R, Python, SAS, or SPSS and familiarity with data visualization tools are typically required. Strong problem-solving skills, critical thinking, and effective communication help convey complex findings to non-technical stakeholders. These skills ensure accurate model development, actionable insights, and effective decision-making based on data.

What is the difference between Statistical Modeling vs Data Analyst?

AspectStatistical ModelingData Analyst
Required CredentialsDegree in statistics, mathematics, or related field; proficiency in statistical softwareDegree in data science, statistics, or related; strong analytical skills
Work EnvironmentResearch, academia, or data-driven industries; focus on model developmentBusiness, marketing, or finance; focus on data interpretation and reporting
Employer & Industry UsageUsed in industries requiring predictive models and complex analysisUsed across various industries for data reporting and insights

Statistical Modeling involves creating mathematical models to understand data patterns and make predictions, often requiring advanced statistical knowledge. Data Analysts focus on interpreting data, generating reports, and providing actionable insights. While both roles work with data, Statistical Modeling emphasizes model development, whereas Data Analysts concentrate on data interpretation and presentation.

Is AI replacing statisticians?

Statisticians play a key role in designing experiments, analyzing data, and interpreting results, and AI tools are used to enhance these tasks rather than replace the profession. AI can automate routine data processing, but statisticians are needed for complex modeling, decision-making, and ensuring data quality. Proficiency in statistical software and programming languages like R or Python remains essential for the job.
More about Statistical Modeling jobs
What cities are hiring for Statistical Modeling jobs? Cities with the most Statistical Modeling job openings:
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What job categories do people searching Statistical Modeling jobs look for? The top searched job categories for Statistical Modeling jobs are:
Statistical Modeling jobs near you
Infographic showing various Statistical Modeling job openings in the United States as of June 2026, with employment types broken down into 96% Full Time, and 4% Part Time. Highlights an 85% Physical, 3% Hybrid, and 12% Remote job distribution, with an average salary of $55,350 per year, or $26.6 per hour.
Sr Statistical Modeler

Sr Statistical Modeler

LexisNexis

Remote

$125K - $209K/yr

Full-time

Posted 10 days ago

LexisNexis rating

7.9

Company rating: 7.9 out of 10

Based on 16 frontline employees who took The Breakroom Quiz

111th of 437 rated business services

Job description

About the Business
LexisNexis Risk Solutions is the essential partner in the assessment of risk. Within our Business Services vertical, we offer a multitude of solutions focused on helping businesses of all sizes drive higher revenue growth, maximize operational efficiencies, and improve customer experience. Our solutions help our customers solve difficult problems in the areas of Anti-Money Laundering/Counter Terrorist Financing, Identity Authentication & Verification, Fraud and Credit Risk mitigation and Customer Data Management. You can learn more about LexisNexis Risk at https://risk.lexisnexis.com/
About the Role:
The Sr Statistical Modeler develops and implements analytics and AI solutions that support business and product objectives across LexisNexis Risk Solutions. This role independently executes complex analytical work, translating well defined problem statements into scalable machine learning solutions across the full modeling lifecycle.
About the Team: This team is intentionally AI forward, suited for a practitioner with hands on experience building, operationalizing, and integrating models into production systems. The role collaborates closely with engineering, product, and platform teams and communicates analytical insights to both technical and non technical stakeholders.
Responsibilities:
  • Applies and integrates statistical, mathematical, predictive modeling and business analysis skills to manage and manipulate complex data from a variety of sources
  • Develops and maintains infrastructure systems that connect internal data sets; creates new data collection frameworks for structured or unstructured data
  • Recognized expert within the function
  • Requires specialized depth and/or breadth of expertise Interprets internal or external business issues and recommends best practices
  • Works independently, with guidance in only the most complex situations
  • Trains/mentors junior staff
  • Serves as an expert of own discipline to clients
  • Interprets internal/external business challenges and recommends best practices to improve products, processes or services

Requirements:
  • Proven Data Science experience, Advanced academic experience-such as a Master's degree in a related discipline-may substitute for part of the required experience
  • Solid experience in applying machine learning and statistical techniques to real-world problems.
  • Hands-on experience developing, evaluating, and iterating on predictive and machine learning models.
  • Experience evaluating model performance using appropriate statistical and machine learning metrics and validation techniques.
  • Experience working with structured and unstructured data at scale.
  • Proficiency in Python and/or R using common data science and machine learning libraries (e.g., pandas, NumPy, scikit-learn, XGBoost, PyTorch).
  • Experience working with SQL and relational or cloud-based data platforms.
  • Hands-on experience developing and running data science and AI workloads in cloud environments such as AWS and Azure, including compute, storage, monitoring, and cost-aware execution.
  • Exposure to modern AI frameworks and tools, including large language model (LLM)-based solutions and retrieval-augmented workflows.
  • Experience training, fine-tuning, or evaluating neural network-based models as part of applied machine learning solutions.
  • Experience in applying software engineering best practices to data science codebases, including testing, code quality checks, and version control workflows.
  • Ability to independently execute complex analytical work within defined scope.
  • Clear and effective communicator, able to explain technical ideas in a way that's easy for non-technical audiences to understand.

Learn more about the LexisNexis Risk team and how we work: https://relx.wd3.myworkdayjobs.com/RiskSolutions/page/21c296c982531000b79663f3194b0000
U.S. National Base Pay Range: $104,900 - $174,700. Geographic differentials may apply in some locations to better reflect local market rates.Base Pay Range for CO is $104,900 - $174,700. Base Pay Range for IL is $110,100 - $183,500. Base Pay Range for Chicago, IL is $115,400 - $192,200. Base Pay Range for MD is $110,100 - $183,500. Base Pay Range for NY is $115,400 - $192,200. Base Pay Range for New York City is $125,900 - $209,700. Base Pay Range for Rochester, NY is $104,900 - $174,700. Base Pay Range for OH is $99,700 - $166,000. Base Pay Range for NJ is $123,816- $197,784.This job is eligible for an annual incentive bonus.Application deadline is 07/30/2026.
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