Research & Development
- Explore and incorporate new features into existing models to improve the performance.
- Built robust feature extraction that is proper for machine-learning tasks.
- Skill in efficiently implementing algorithms that are published in technical papers and/or innovating algorithms.
- Demonstrated creative and critical thinking with an innate drive to improve how things work.
- Use a flexible, analytical approach to design, develop and evaluate predictive models and advanced algorithms that lead to optimal value extraction.
- Hands-on data analysis experience and ability to produce data visualization to present complex data graphically (e.g. distributions, scatter plots, sensitivity analyses).
- Publish a high quality proceedings or journals in the feature engineering domain at least 1 publication per year.
- PhD in Engineering, Computer Science, Applied Mathematics, Advanced Statistics, Biostatistics, or related field.
- Enthusiasm for continuing to learn state-of-the-art techniques in machine learning.
- Experience in machine-learning technique optimization (linear/nonlinear programming, genetic algorithms, support vector machines, ensembling, etc.), hierarchical and non-hierarchical clustering techniques (K-means, agglomerative clustering, divisive clustering, graph theory), factor analysis, sensitivity analysis, Principal Components Analysis (PCA)
- Strong background in a variety of machine-learning domains, such as deep learning, ASR, NLP, robotics, computer vision, gesture recognition, multimodal fusion, etc.
- Fluent in at least one programming language (e.g. Python) and are comfortable developing code within a team environment (e.g. git).
- Self-motivated and curious, continue to learn on the job.
- Excellent interpersonal and collaboration skills.
- Able to work and be optimistic under very high pressure.