Data Scientist

Job Responsibilities:

• Conduct independent (but complementary) research, build, and deploy scalable models in machine learning, artificial intelligence and statistics; implement new state-of-the-art algorithms and/or hardware systems to support research and development goals.
• Encouraged to think out-of-the-box, innovate and find solutions to real-life problems.
• Work with software team on designing, implementing, and analyzing techniques.
• Contribute to and actively participate in the conception, design, and execution of research to address defined problems.
• Document methods, procedures, and results in compliance with ISO/FDA regulatory standards and recommendations.
• Collaborate with other specialists and experts in a multidisciplinary team environment to accomplish research goals.
• Publish novel research results in peer-reviewed scientific or technical journals and present results at external conferences, seminars, and technical meetings at least 2 publications per year.
• Develop new conceptual frameworks from an initial design to a market release.
• Cooperate proof-of-concept (POC) tasks with technicians/engineers.
• Experience as a decision maker in selecting innovative, practical methods to achieve problem resolutions.
• Conduct feasibility studies to verify capability and functionality

Qualifications:

• PhD in Engineering, Computer Science, Applied Mathematics, Advanced Statistics, Biostatistics, or related field.
• Demonstrated state-of-the-art knowledge of one or more significant topics as below,
• Feature extraction — experience in image features and/or clinical/biomedical/medical attributes etc.
• Image processing/computer vision — particularly applications related to analysis of facial features (detection, recognition, classification), body motion tracking and movement classification, activity analysis, and/or real-time image effect processing (dynamic morphing/blending texture maps, “face replacement”).
• Health informatics/biostatistics — particularly “big data” analysis, public datasets to understand health-related trends.
• Experience in machine learning techniques and advanced analytics (e.g. regression, classification, clustering, time series, causal inference, mathematical optimization, neural nets).
• Ability to develop independent research projects as demonstrated through publication of peer-reviewed literature.
• Proficient verbal and written communication skills to collaborate effectively in a team environment and present and explain technical information.
• Initiative and interpersonal skills and ability to work in a collaborative, multidisciplinary team environment.
• Experience with cloud computing resources and data analysis techniques (e.g. Hadoop MapReduce, Spark).
• Expertise with R and Python
• Able to work and be optimistic under very high pressure.