Career Center > Careers in Statistics > Which Industries Employ Statisticians


Statistical methods are applied to a range of chemistry problems, from traditional laboratory experimentation to new techniques in molecular design. Statisticians contribute to chemistry problems in pharmaceuticals, electronics and semiconductors, paints and coatings, agriculture, food science, and many other interesting fields. Typical applications include the following:

  • Collaborate with food scientists and test kitchen staff to optimize a recipe. The statistician must apply experimental designs for mixtures of ingredients, taste-testing using hedonic scales, and numerical and graphical optimization techniques.
  • Build response surface models to describe the performance of a chemical formulation in terms of its component levels. This may begin as a classic factorial study, and then blend chemical knowledge with empiricism to develop meaningful models. Ultimately, nonlinear mechanistic models may be identified to describe the chemical kinetics.
  • Predict the properties of compounds that have not yet been synthesized in any lab by supporting computer-aided molecular design based on molecular property descriptors. Statisticians develop optimum design strategies that minimize the number of syntheses required. Innovative modeling techniques enable continuous refinement of the molecular models to provide better predictions of the most promising compounds.
  • High-throughput testing generates unprecedented volumes of data on both the synthesis conditions and evaluated performance of each compound. High-dimensional data visualization and mining techniques ensure that the valuable information in these data will be discovered.
  • Analytical chemists often generate spectra instead of single-number results. Chemometric techniques draw upon multivariate statistics to reduce large sets of numbers to a meaningful few.

Statistical methods are well accepted in traditional chemical research. Statisticians today face the exciting challenge of developing design and modeling techniques to keep up with rapid developments in high-throughput testing, quantitative structure-property relationship modeling, and rapid analytical testing.