Provide stats guidance and direction across the MSAT DP network on approaches and continuous improvement plans.
Provide statistical guidance for method validations and method transfers in the areas of accuracy, precision, linearity, equivalence testing (T OST), power and sample size.
Support major process engineering and/or quality investigations from a statistical perspective to ensure data is available to provide focused information as part of the problem- solving processes.
Develop the sites statistical process control (SPC) systems to help process engineers build a data led understanding, including OCS/PCS, of the state of control of different manufacturing and laboratory processes.
Interface with site’s PCS team and site’s automation team to understand how data is captured and how available data relates to product and process critical process parameters.
Support the building of statistically based sampling plans in inspection areas.
Use the appropriate statistical tools for analysing many types of data problems. These problems can range from selecting random data samples to designing statistical experiments.
Construct and apply various prediction (regression) equations including the application of statistical process control procedures, covering univariate and multivariate samples.
Understand and deploy processes for data presentation (frequency tables, histograms and box plots), descriptive statistics (mean, median, mode, range, variance, standard deviation, quartile and quantile), basic normal probability theory and statistical hypothesis testing. Several one-sample testing procedures—such as the binomial test, normal test and the t-test—are detailed, along with confidence intervals. The appropriate tests of hypotheses for (one -sample) variance problems are also included.
Analyse and interpret statistical data to identify significant differences in relationships among sources of information.
Develop an understanding of fields to which statistical methods are to be applied to determine whether methods and results are appropriate.
Evaluate sources of information to determine any limitations in terms of reliability or usability.
Evaluate the statistical methods and procedures used to obtain data to ensure validity, applicability, efficiency, and accuracy.
Identify relationships and trends in data, and any factors that could affect the results of research.
Plan data collection methods for specific projects and determine the types and sizes of sample groups to be used.
Provide guidance on preparation of data for processing by organising information, checking for any inaccuracies, and adjusting and weighting the raw data.
Lead & provide guidance on reporting of results of statistical analyses, including information in the form of graphs, charts, and tables.
Design research projects that apply valid scientific techniques and use information obtained from baselines or historical data to structure uncompromised and efficient analyses.
Develop and test experimental designs, sampling techniques, and analytical methods.
Examine theories, such as those of probability and inference, to discover mathematical bases for new or improved methods of obtaining and evaluating numerical data.
Establish and maintain internal / external network relationships in the area of statistics.
