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Statistical Data Analysis Jobs (NOW HIRING)

Bachelor degree in statistics, data analytics, MBA, Masters in Statistics, Masters in Data Science or equivalent TekWissen ® Group is an equal opportunity employer supporting workforce diversity.

Data Analyst

Washington, DC · On-site

$61 - $68/hr

Duties: • Analyzes information and statistical data to prepare reports and studies for use by professionals. • Creates charts and graphics to present statistical analysis in an easily digestible ...

Experiencein a Business Analyst or related role involving data analysis StrongExcel skills including charting, Pivot tables, statistical and data cleanupfunctions BasicPowerPoint, Word, Outlook ...

Top 3 Keywords: 1- Process Capability Analysis & Statistical Data Tracking 2- Deviation Management (Root Cause & Corrective Actions) 3- Microsoft Office (Advanced Excel) Top Required Skills:

Fundamental statistical, data analysis and visualization skills Strong communication skills Desirable Skills and Experience: * Ability to create SQL or MS Access queries and reports. * Experience ...

Data Analyst

Washington, DC · On-site

$71 - $79/hr

Duties: • Analyzes information and statistical data to prepare reports and studies for use by professionals. • Creates charts and graphics to present statistical analysis in an easily digestible ...

Ability to explain data types, summary statistics, correlation analysis, and visualization best practices while preparing students for business analysis roles, analytics positions, and data-driven ...

Ability to explain data types, summary statistics, correlation analysis, and visualization best practices while preparing students for business analysis roles, analytics positions, and data-driven ...

Ability to explain data types, summary statistics, correlation analysis, and visualization best practices while preparing students for business analysis roles, analytics positions, and data-driven ...

Ability to explain data types, summary statistics, correlation analysis, and visualization best practices while preparing students for business analysis roles, analytics positions, and data-driven ...

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Statistical Data Analysis information

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How much do statistical data analysis jobs pay per hour?

As of Jun 13, 2026, the average hourly pay for statistical data analysis in the United States is $38.47, according to ZipRecruiter salary data. Most workers in this role earn between $24.28 and $50.24 per hour, depending on experience, location, and employer.

What is statistical data analysis?

Statistical data analysis is the process of collecting, organizing, interpreting, and presenting numerical data using statistical methods. It helps researchers and professionals identify patterns, trends, and relationships within data sets, enabling informed decision-making. Common techniques include descriptive statistics, hypothesis testing, regression analysis, and data visualization. Statistical data analysis is widely used in fields such as business, healthcare, social sciences, and engineering to draw meaningful conclusions from data.

Are statisticians highly paid?

Statisticians typically earn higher-than-average salaries due to their specialized skills in data analysis, modeling, and statistical software. Salaries vary based on experience, education, industry, and location, but the profession is generally considered well-compensated within the data analysis field.

What does a statistical data analyst do?

A statistical data analyst collects, processes, and interprets large datasets to identify trends and support decision-making. They use statistical software and techniques to analyze data, create reports, and communicate findings to stakeholders, often requiring strong analytical skills and knowledge of tools like Excel, R, or Python.

What is the difference between Statistical Data Analysis vs Data Scientist?

AspectStatistical Data AnalysisData Scientist
Required CredentialsDegree in Statistics, Mathematics, or related fieldDegree in Computer Science, Statistics, or related field
Work EnvironmentResearch labs, finance, healthcare, academiaTech companies, finance, e-commerce, startups
Employer & Industry UsageUsed across industries for analyzing data setsUsed for building predictive models and data-driven products

Statistical Data Analysis focuses on interpreting data using statistical methods, often emphasizing hypothesis testing and descriptive statistics. Data Scientists combine statistical analysis with programming and machine learning skills to create predictive models and extract insights from large, complex data sets. While both roles analyze data, Data Scientists typically have broader technical skills and work on developing algorithms, whereas Statistical Data Analysts concentrate on statistical techniques and reporting.

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

To thrive as a Statistical Data Analyst, a solid background in statistics, mathematics, and data interpretation is required, often supported by a degree in statistics, mathematics, or a related field. Expertise in statistical software such as R, Python, SAS, or SPSS, along with experience in data visualization tools, is typically essential. Strong analytical thinking, attention to detail, and effective communication skills help analysts present complex findings clearly to stakeholders. These competencies are crucial for extracting actionable insights from data and supporting informed, data-driven business decisions.

Is AI replacing data analysts?

AI is transforming the role of data analysts by automating routine tasks such as data cleaning and basic analysis, allowing analysts to focus on more complex insights and strategic decision-making. While AI tools can augment their work, human expertise remains essential for interpreting results, understanding context, and making informed recommendations. Data analysts who develop skills in machine learning, programming, and data visualization are better positioned to adapt to these technological changes.

Is 40 too late for data science?

Statistical Data Analysis is a key skill in data science, and individuals can successfully transition into the field at age 40 or later. Many data scientists have diverse backgrounds and acquire necessary skills such as programming, statistics, and machine learning through online courses or certifications at any age.

What are some common challenges faced by professionals in statistical data analysis roles, and how can they be addressed?

Professionals in statistical data analysis often encounter challenges such as dealing with incomplete or messy datasets, selecting appropriate statistical methods, and ensuring the reproducibility of their analyses. Overcoming these obstacles typically involves rigorous data cleaning, continuous learning about statistical techniques, and using version control systems for code and data. Collaboration with subject matter experts and maintaining clear documentation are also essential for ensuring that findings are accurate and actionable.
More about Statistical Data Analysis jobs
What are the most commonly searched types of Statistical Data Analysis jobs? The most popular types of Statistical Data Analysis jobs are:
What states have the most Statistical Data Analysis jobs? States with the most job openings for Statistical Data Analysis jobs include:
Statistical Data Scientist

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 27 days ago


Job description

Location:  This position may be performed remotely, but requires the flexibility and willingness to travel as needed.

The Opportunity

Praxis is seeking an Associate Director, Statistical Data Scientist to lead and execute the development, validation and automation of analytical pipelines and statistical models that support metadata-driven clinical data processing, reporting, and regulatory submissions. 

This is a hands-on technical leadership role, ideal for a senior data scientist or statistical programmer who enjoys coding, problem-solving, and working cross-functionally to bring rigor, reproducibility, and automation to clinical reporting workflows. 

The Associate Director will contribute directly to R/Python development while also setting technical standards, mentoring peers, and ensuring readiness for R-based regulatory submissions. 

Primary Responsibilities

  • Lead the design, development, and validation of R/Python code to automate generation of analytical datasets and TLFs within a metadata-driven pipeline. 
  • Translate SAPs and metadata specifications (YAML/CSV) into executable and reproducible code. 
  • Build and validate R packages and data science tools supporting both exploratory and confirmatory analyses, ensuring full traceability and audit readiness. 
  • Implement and validate statisticall models (e.g., MMRM, ANCOVA, logistic regression) using R packages such as mmrm, emmeans. 
  • Collaborate with IT to integrate data science and statistical programming workflows within Databricks and CI/CD pipelines for continuous validation and reproducibility 
  • Collaborate across programming, biostatistics, and data standards functions to ensure dataset definitions, derivations, and metadata align with controlled standards. 
  • Conduct peer code reviews, unit testing, and automated validation; ensuring deliverables meet submission-quality and reproducibility standards 
  • Mentor and guide team members in best practices for programming, validation, and automation  

Qualifications and Key Success Factors

  • Bachelor's or Master's degree in Statistics, Biostatistics, Data Science, or a related field. 
  • 8+ years of statistical programming experience in the pharmaceutical/biotech industy including hands-on experience with R and/or Python. 
  • Proven experience preparing or supporting R-based regulatory submissions (e.g., R package validation, R-based analysis delivery, or submission readiness) 
  • Strong understanding of CDISC ADaM and SDTM data structures, and their use in analytical workflows 
  • Experience developing and validating reusable R/Python libraries and functions 
  • Proficiency with Git, Bitbucket, and CI/CD automation pipelines 
  • Working knowledge of GxP and Part 11 compliance 
  • Excellent documentation and validation practices 
  • Collaborative and proactive mindset; able to operate independently in a small, agile team. 

Preferred Experience: 

  • Familiarity with YAML/JSON configuration and metadata-driven programming workflows 
  • Prior experience migrating from SAS to R/Python environments 
  • Knowledge of R validation frameworks (e.g., risk-based testing, reproducibility documentation). 
  • Experience with exploratory analytics or visualization in R or Python within a regulated framework. 

The physical and mental requirements of our roles include but are not limited to regular use of a computer, devices or other office equipment, clear communication, and occasional movement.  You'll need comfort with screen work, basic hand coordination, and focus.  Reasonable accommodations may be made to enable individuals with disabilities to perform these functions.

Compensation & Benefits

At Praxis, we believe that taking care of our people (and their people) is important, so we provide a world class benefits package to help you thrive. This includes 99% of the premium paid for medical, dental and vision plans.  We also provide company-paid life insurance, AD&D, disability benefits, and voluntary plans to personalize your coverage.Thinking about the future? We match dollar-for-dollar up to 6% on eligible 401(k) contributions and sweeten the deal with long-term stock incentives and ESPP. We provide a discretionary quarterly bonus, an extremely flexible wellness benefit, generous PTO, paid holidays and company-wide shutdowns. Not to mention, you'll also be joining a phenomenal crew of colleagues who are smart, engaged and inspiring. We aim high, collaborate hard, and produce results. Let's achieve the impossible together! 

To round out our world-class total rewards package, we provide annualized base salary compensation in the range listed below.  This range reflects the base salary the Company reasonably expects to pay for the position at the time of posting. Placement within the range will be based on job-related factors, including experience, qualifications, scope of responsibilities, and demonstrated track record of delivering results in similar roles.