Meridian Innovation is seeking passionate and talented individuals to help us realize our mission, to enable thermal imaging solutions for safer and better living. As a Data Scientist, you will accumulate and analyze large data sets for the purpose of improving our thermal image processing. You will be responsible for the development of methods and pipelines for next- generation thermal imaging sequencing data, build machine and deep learning models. Develop new ways to process and manage thermal imaging data so it can be used for local and cloud AI processing. We prefer someone who has previously worked on image processing or pattern recognition with strong statistical data analytics background. Knowledge of thermal or spectrometer signal is also a plus.
Strong experience with statistical programming languages such as Python, R, Spark ML library, Matlab and similar technologies.
Ability to understand applications objectives and collaborate on data centric solutions to common engineering problems.
Acquire and Work with large and complex datasets.
Detect and correlate anomalies across disparate time-series data.
Build and prototype data processing pipelines.
Create and test analytic model (s) to be used against live data.
Evaluate and train various models to detect and cluster anomalies.
Assign statistical significance/confidence interval to anomalies.
Recommend and test predictive models and machine-learning algorithms.
Ph.D. in a quantitative field such as Statistics, Physics, Computational Biology, Computer Science, Mathematics, or related discipline.
2+ years meaningful work or research experience in data analysis or similar field (e.g., as a statistician / data scientist /etc).
Demonstrated experience in using deep learning frameworks in your work.
Recognized for technical leadership contributions, capable of self-direction, and a
willingness to learn from and teach others.
Conduct end-to-end analysis including design, data gathering, processing, analysis, iteration with collaborators, and dissemination of results.
Collaborate with multiple AI partners to understand their workflow requirements and tradeoffs between flexibility/model exploration.