Lead Statistical Analyst information
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$60.5K - $70.9K
4% of jobs
$70.9K - $81.3K
6% of jobs
$81.3K - $91.7K
7% of jobs
$96.6K is the 25th percentile. Wages below this are outliers.
$91.7K - $102.1K
16% of jobs
$102.1K - $112.5K
9% of jobs
The median wage is $120.6K / yr.
$112.5K - $123K
9% of jobs
$123K - $133.4K
13% of jobs
$133.4K - $143.8K
8% of jobs
$147.2K is the 75th percentile. Wages above this are outliers.
$143.8K - $154.2K
6% of jobs
$154.2K - $164.6K
13% of jobs
$164.6K - $175K
8% of jobs
How much do lead statistical analyst jobs pay per year?
As of Sep 9, 2026, the average yearly pay for lead statistical analyst in the United States is $123,849.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,000.00 and $153,500.00 per year, depending on experience, location, and employer.
A Lead Statistical Analyst oversees the design, execution, and interpretation of complex statistical analyses for an organization. They are responsible for leading a team of analysts, ensuring data integrity, selecting appropriate statistical methods, and communicating findings to stakeholders. Their work often supports decision-making in areas like business strategy, research, or product development. The role also involves mentoring junior analysts and collaborating with other departments to meet organizational goals.
To thrive as a Lead Statistical Analyst, you need advanced statistical knowledge, expertise in data analysis, and a degree in statistics, mathematics, or a related field, often with experience in leadership roles. Proficiency with statistical software like SAS, R, Python, and familiarity with data visualization tools and database management systems is typically required. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for interpreting complex data and guiding teams. These abilities ensure accurate analysis, actionable insights, and successful collaboration on data-driven projects.
Lead Statistical Analysts often encounter challenges such as ensuring data integrity across multiple sources, managing tight project deadlines, and communicating complex statistical findings to non-technical stakeholders. Addressing these issues typically involves establishing robust data validation processes, setting clear project milestones, and collaborating closely with both data teams and business units. Strong leadership and effective communication skills are essential for navigating these challenges while maintaining high-quality analysis and fostering team collaboration.
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