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Statistical Inference Jobs (NOW HIRING)

Working independently in a dynamic, data-driven environment, you will leverage methods from multiple disciplines, including statistical inference, machine learning, and simulation frameworks, to ...

Responsibilities : • Participating in diverse projects involving exploratory data analysis, statistical inference, and predictive modeling • Applying risk analysis methodologies to problems in ...

Participate in exploratory data analysis, statistical inference, and predictive modeling projects. * Apply risk analysis to problems in engineering, health, finance, ecology, and the environment.

Working independently in a dynamic, data-driven environment, you will leverage methods from multiple disciplines, including statistical inference, machine learning, and simulation frameworks, to ...

Data Scientist

San Francisco, CA · On-site

$140 - $210/hr

Strong command of SQL, Python, and Git, alongside expertise in statistical inference, experimental design, and predictive modeling * A proven ability to turn ambiguous business questions into ...

Data Scientist III

Charlottesville, VA · On-site

$98K - $171K/yr

You'll apply statistical inference, state-space modeling, and Monte Carlo methods to multi-target tracking challenges that combine mathematical rigor with operational constraints. Your work will span ...

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Statistical Inference information

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How much do statistical inference jobs pay per hour?

As of Aug 22, 2026, the average hourly pay for statistical inference in the United States is $56.31, according to ZipRecruiter salary data. Most workers in this role earn between $41.11 and $71.88 per hour, depending on experience, location, and employer.

What is statistical inference?

Statistical inference is the process of using data analysis to deduce properties of an underlying probability distribution. It involves making predictions or generalizations about a population based on a sample. Common methods include hypothesis testing, confidence intervals, and estimation. These techniques help researchers and analysts draw meaningful conclusions from limited data, accounting for randomness and uncertainty.

What are some common challenges faced by professionals working in statistical inference roles?

Professionals in statistical inference often face challenges such as ensuring data quality, dealing with incomplete or messy datasets, and selecting appropriate models for analysis. Interpreting results accurately and communicating complex statistical findings to non-technical stakeholders can also be demanding. Additionally, keeping up with advances in statistical methodologies and software tools is essential for continued professional growth in this field.

What are the key skills and qualifications needed to thrive as a statistical inference specialist, and why are they important?

To thrive as a Statistical Inference Specialist, you need strong mathematical and statistical knowledge, a relevant degree (such as statistics, mathematics, or data science), and experience with probability theory and hypothesis testing. Familiarity with statistical software like R, Python (with libraries such as SciPy and statsmodels), and tools like SAS or SPSS is typically required. Critical thinking, problem-solving, and clear communication skills enable you to interpret data accurately and convey findings to various stakeholders. These skills and qualities are crucial for drawing valid conclusions from data, supporting evidence-based decision-making, and ensuring the integrity of research or business analyses.

What is the difference between Statistical Inference vs Data Analyst?

AspectStatistical InferenceData Analyst
Primary FocusDrawing conclusions from data samplesAnalyzing and interpreting data to inform business decisions
Skills & CertificationsStatistics, probability, hypothesis testing, certifications like SAS or RData visualization, SQL, Excel, often with certifications like Microsoft Excel or Tableau
Work EnvironmentResearch institutions, academia, data science teamsBusiness, marketing, finance departments
Usage in IndustryDesigning experiments, making inferences about populationsReporting insights, creating dashboards, data cleaning

While both roles involve working with data, Statistical Inference focuses on making conclusions from data samples using statistical methods, often in research settings. Data Analysts interpret data to support business decisions, emphasizing data visualization and reporting. Understanding these differences helps clarify career paths and job expectations in data-related fields.

What does statistical inference do?

Statistical inference is a key part of the statistical inference job, involving the process of drawing conclusions about a population based on sample data. It includes techniques such as hypothesis testing, estimation, and confidence intervals to make data-driven decisions and predictions. Proficiency in statistical software and understanding of probability are essential for performing these tasks effectively.
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What job categories do people searching Statistical Inference jobs look for?

The top searched job categories for Statistical Inference jobs are:

Infographic showing various Statistical Inference job openings in the United States as of August 2026, with employment types broken down into 1% Internship, 83% Full Time, 14% Part Time, and 2% Contract. Highlights an 81% Physical, 3% Hybrid, and 16% Remote job distribution, with an average salary of $117,120 per year, or $56.3 per hour.

Measurement Framework Developer - Data Scientist (Statistical & Causal Inference)

Ampcus Inc

Remote

Full-time

Re-posted 19 days ago


Job description

Job Summary:
Ampcus Inc. is a certified global provider of a broad range of Technology and Business consulting services. We are looking for a Data Scientist with strong expertise in statistical inference and causal analysis to develop measurement frameworks for enterprise solutions. This role involves designing experiments, applying causal inference methods, and building scalable frameworks to measure impact.
Responsibilities:
• Design and implement measurement frameworks for solutions in production.
• Apply statistical inference and causal methods (e.g., A/B testing, propensity score matching, instrumental variables).
• Develop and analyze controlled experiments and observational studies.
• Collaborate with stakeholders to define KPIs and measurement strategies.
• Write clean, reproducible code for statistical analysis and reporting.
• Implement CI/CD principles and manage code repositories using GitHub Enterprise.
Qualifications:
Required:
• Strong knowledge of hypothesis testing, OLS, GLM, and causal inference techniques.
• Proficiency in Python and SQL; experience with libraries like statsmodels, scikit-learn, DoWhy, linearmodels.
• Experience with A/B testing and experimental design.
• Familiarity with Databricks or similar enterprise cloud environments.
• Self-starter with an ownership mindset and ability to work independently.
Preferred:
• Experience in retail, inventory management, or operations research.
• Exposure to cloud platforms (Azure, AWS, GCP).
Company:
Ampcus is a global business, technology consulting and an staff augmentation firm specializing in AI/ML,digital solutions, Cybersecurity & Risk management, Testing, Forensics & Fraud services and human capital management. Founded in 2004, the company is headquartered in Chantilly, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

Ampcus logo

About Ampcus

Sourced by ZipRecruiter

Ampcus Inc. is a ISO 20000, ISO 27000, ISO 9001, CMMI DEV/3 SM and CMMI SVC/3 SM certified global provider of a broad range of Technology and Business consulting services. From strategy to execution, our disciplined yet flexible approach starts and ends with our clients. By listening hard and working harder, client goals become our goals. Their success is our satisfaction. It’s why our clients sleep well at night. We believe that the success of an engagement is determined by strong project management, as well as clear communication and mutual commitment working collaboratively. Our methodology begins with listening to the customer about their needs, then working with their team to gain a clear understanding of the requirements, while providing knowledge transfer of best practices for the organization.

Industry

It services

Company size

1,001 - 5,000 Employees

Headquarters location

Chantilly, VA, US

Year founded

2004