At CADDi, we have a massive amount of technical drawings, corresponding quotations, along with data describing the required manufacturing processes. By processing these data into a form that is easier to utilize and quickly implementing the cycle of hypothesis testing, we aim to find answers to the problems faced by the manufacturing industry.
Besides what we already have, there is a lot more data out there that hasn’t been fully utilized. Assessing valuable data and preparing it for utilization is the key to taking the burden off this industry.
- Leveraging existing data assets to discover new value for the corporation
- Analysis of base data to obtain hypotheses concerning business and operational improvement
- Developing new user-facing features that combine CADDi’s domain expertise, data assets, and operational excellence
- An understanding our mission to unleash the potential of manufacturing
- 3+ years of experience utilizing machine learning methods to solve business problems
- Experience delivering services in a production environment in the cloud (AWS, GCP, Azure, etc)
- Self-driven, and can operate both independently and also as a team member
- Able to exercise strong problem solving skills to deliver customer value
- Strong fundamental skills in linear algebra, calculus, and statistics
- Experience in application development or analysis work with Python, MATLAB, or R
- Experience with machine learning frameworks (PyTorch, Tensorflow, scikit-learn, LightGBM, etc)
- Experience using SQL for data analysis
- Experience using version control systems such as Git
Nice to haves
These aren’t required, but be sure to mention them in your application if you have them.
- Experience with GPU-based data processing (CUDA, OpenCL, etc)
- Experience working with data pipeline technologies such as Airflow, Apache Beam, Spark, etc.
- Past participation in data analysis contests such as Kaggle
- Comfortable reading research papers from major journals
- Experience utilizing numerical optimization methods (continuous, discrete, or combinatorial)
5 to 12 million JPY annually.