Research, customization, and development of statistical and machine learning algorithms to meet complex project requirements; tasks include defining hypotheses, executing necessary tests and experiments, evaluating, tuning, and optimizing algorithms and methods to specific situations.
Analysis of big data for data-driven solution validation, evaluation, and technology innovation.
Optimize data analysis processes and systems for better efficiency and maintainability.
Leading sub-functional and small project teams.
Mentoring and training more junior team members and serving as a best-practice resource for statistics and machine learning.
Writing of documents that clearly explain how algorithms should be implemented, verified, and validated.
Writing documents for use in the preparation of intellectual property and technical publications.
Monitoring the literature of interest and industrial development trends, broadly in the areas of data analysis and machine learning.
Understanding regulatory requirements, such as those mandated by the FDA.
Working within the Company’s Quality system, standards and maintaining training requirements.
Being ever mindful of the requirements of the wider market and Company’s stakeholders.
Promoting safe working environment within OH&S guidelines
Qualifications and Experience
Expert in statistical analysis methods, including analysis of variance, regression, time series analysis, survival analysis, etc.
Extensive knowledge in machine learning fundamental theories and data mining technologies.
Leadership and hands-on experience with the development of data analytics systems, including data exploration/crawling, feature engineering, model building, performance evaluation, and online deployment of models.
Proficient with server-side programming in Python/Java.
Hands-on experience in handling large and distributed datasets on Hadoop, Spark, Hive, Pig or Storm, etc.
Strong database skills and experience, including experience with SQL programming.
Knowledge in big data technologies, including cloud computing/distributed computing, data fusion, and data visualization.
Experience in R programming.
A background in or exposure to biomedical engineering, outcomes research, medical science or physiology.
Good technical writing and presentation skills.
Optimization of algorithm complexity vs. accuracy vs. implementation cost.
Implementing robust software for use in research programs with a minimum of review and other formal processes
A degree in Computer Science, Engineering, Statistics, Applied Mathematics, or related fields.
Minimal 6 years' industry or academic experience in data science.
Post-graduate research experience (Masters or PhD) in a field encompassing Data Science, Applied Statistics, Biomedical Informatics, or Outcomes Research.
Relevant industry experience would be favorably considered.