Let's talk about the team and you:
The Advanced Analytics team is headquartered in the United States and is focused on developing analytics solutions that will enable a data-driven approach to addressing business questions. This role will be a part of the Advanced Analytics team in San Diego. It will collaborate with the global Advanced Analytics team in support of achieving global and regional business goals.
The Lead Data Scientist will undertake applied research and development in the areas of data science, biomedical informatics, and outcomes research, using the latest technologies in machine learning and distributed computing. The platform and algorithms developed may be used in a range of diagnostic and therapeutic applications, such as sleep disorder breathing, chronic obstructive pulmonary disorder, and other respiratory disorders, as well as co-morbidities such as congestive heart failure and diabetes and chronic disease management.
Let's talk about Responsibilities:
- Research, customization, and development of statistical and machine learning algorithms to meet unique and complex project requirements that have broad business impacts; 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.
- Coordinate with different functional teams to implement models and monitor outcomes.
- Leading project teams to achieve milestones and objectives.
- Anticipating challenges and issues and recommending process, product, and service improvements.
- Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
- Collaborate with management, stakeholders, and teams to define technology roadmaps.
- Mentoring and training more junior team members and serving as a best-practice resource for statistics, artificial intelligence, and machine learning.
- Writing 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 ResMed Quality system, standards and maintaining training requirements.
- Being ever mindful of the requirements of the broader market and ResMed stakeholders.
- Promoting safe working environment within OH&S guidelines
Let's talk about Qualifications and Experience:
- Expert in statistical analysis methods, including analysis of variance, regression, time series analysis, survival analysis, etc.
- Expert knowledge in artificial intelligence and machine learning fundamental theories and data mining technologies.
- Thorough knowledge of data engineering or informatics systems.
- 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 R programming and server-side programming in Python or 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.
- 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 8 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 favourably considered.
Joining us is more than saying “yes” to making the world a healthier place. It’s discovering a career that’s challenging, supportive and inspiring. Where a culture driven by excellence helps you not only meet your goals, but also create new ones. We focus on creating a diverse and inclusive culture, encouraging individual expression in the workplace and thrive on the innovative ideas this generates. If this sounds like the workplace for you, apply now!