Data Scientist Research Associate information
See Rutherford, NJ salary details
$60.4K is the 25th percentile. Wages below this are outliers.
$58.6K - $65.2K
95% of jobs
$65.2K - $71.9K
1% of jobs
$71.9K - $78.5K
0% of jobs
$78.5K - $85.1K
1% of jobs
$85.1K - $91.7K
2% of jobs
$91.7K - $98.4K
1% of jobs
$105K - $111.6K
0% of jobs
$111.6K - $118.3K
0% of jobs
$118.3K - $124.9K
0% of jobs
$124.9K - $131.5K
0% of jobs
How much do data scientist research associate jobs pay per year?
As of Aug 14, 2026, the average yearly pay for data scientist research associate in Rutherford, NJ is $69,361.00, according to ZipRecruiter salary data. Most workers in this role earn between $60,100.00 and $60,700.00 per year, depending on experience, location, and employer.
To thrive as a Data Scientist Research Associate, you need a solid background in statistics, machine learning, and data analysis, generally supported by a degree in computer science, statistics, or a related field. Familiarity with programming languages such as Python or R, experience with data visualization tools, and knowledge of platforms like SQL and TensorFlow are typically required. Strong problem-solving abilities, attention to detail, and effective communication skills help you excel in collaborative research environments. These skills and qualities are crucial for generating actionable insights from data and advancing research objectives.
A Data Scientist Research Associate is a professional who applies statistical analysis, machine learning, and data management techniques to conduct research and extract insights from complex datasets. They often work in academic, government, or industry research settings, supporting projects by designing experiments, analyzing large volumes of data, and interpreting results. Their work helps inform decision-making, advance scientific knowledge, and develop new data-driven solutions. Typically, they collaborate with multidisciplinary teams and may contribute to publishing research findings.
Data Scientist Research Associates often collaborate with experts from fields such as engineering, biology, or social sciences. A common challenge is bridging the communication gap between disciplines, as each may have its own jargon and methodologies. Additionally, integrating and preprocessing diverse datasets can be complex due to varying data formats and quality. Overcoming these challenges requires strong communication skills, adaptability, and a willingness to learn about domain-specific concepts to ensure productive teamwork and successful project outcomes.
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