Overview In this Statistical Scientist role within Exponent's Data Sciences Practice, you apply exploratory data analysis, statistical inference, and predictive modeling to diverse projects. You will support data collection planning, experimental design, and risk analysis across engineering, health, and environment domains. You'll develop reports and visualizations to address client problems and support litigation. This is a cross disciplinary, impact driven role at a firm that values collaboration and growth.
Compensation / Benefits- programs supporting health and well-being
- competitive benefits
- retirement benefits with 401(k) employer contribution of 7%
- bonuses (bi-weekly and annual)
- career growth and sponsorship
Responsibilities- Participate in diverse projects involving exploratory data analysis, statistical inference, and predictive modeling
- Apply risk analysis methodologies to problems in engineering, health, finance, ecology, and the environment
- Support clients in planning data collection: framing the problem, experimental design, sample size calculations, and identifying populations and sampling strategies
- Develop reports and visualizations to address clients' scientific/engineering problems and support litigation
Key requirements- Ph.D. in Statistics, Biostatistics, or a related field
- Excellent communication skills and the ability to explain statistical concepts to non-technical audiences
- Experience or capabilities in statistical data mining, analysis of reliability and life data, experimental design, Bayesian statistics, machine learning, or quality control/improvement
- Minimum of 2 years' experience with statistical software (SAS and R preferred)
- Programming skills (e.g., C++, SQL, Visual Basic, Python) and dashboard development experience are strongly preferred
- Authorized to work in the United States without sponsorship
- Excellent communication with non-technical audiences
- Independent work and cross-functional collaboration
- Multidisciplinary teamwork
- Statistical data mining
- Reliability and life data analysis
- Experimental design
