Share
An ideal candidate will be an expert in the areas of machine learning, operations research, and statistics. With expertise in applying theoretical models in an applied environment relying heavily on the latest advances in machine learning, optimization, stochastic modeling, and engineering. The candidate will be expected to work on numerous aspects, such as feature engineering, modeling, probabilistic modeling, hyper-parameter tuning, scalable inference methods and latent variable models. Challenges will involve dealing with very large data sets and requirements on throughput.Key job responsibilities
- Create experiments and prototype implementations of new learning algorithms and prediction techniques
- Master's degree in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
- 3+ years of building machine learning models or developing algorithms for business application experience
- 3+ years of solving business problems through machine learning, data mining and statistical algorithms experience
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java, C++, Python or related language
- Experience in professional software development
- Experience implementing algorithms using toolkits and self-developed code
- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
- 5+ years of building machine learning models or developing algorithms for business application experience
- Knowledge of architectural concepts and algorithms, schedule tradeoffs and new opportunities with technical team members
These jobs might be a good fit