Machine Learning Job Description Tokyo

We are looking for an expert in machine learning to help us extract value from our data. You will lead all the processes from data collection, cleaning, and preprocessing, to training models and deploying them to production.

The ideal candidate will be passionate about artificial intelligence and stay up-to-date with the latest developments in the field. Prior experience in Machine Learning projects would be a plus.

The Machine Learning Engineer will have a strong analytic background, typically in computer science or mathematics, and the application of statistics. Computer science programming expertise is desirable to analyze the data and provide intelligence that leads to better business decisions or new products.

The Machine Learning Engineer investigates, recommends, and initiates acquisition of massive streams of data from internal and external sources. Due to the volume of data, its multi-structured nature, and the various advanced analysis involved, MapReduce and other advanced software available with, for example, Teradata Vantage and Hadoop are used to supplement or in place of traditional SQL analysis. Data can originate anywhere; from sales records, web logs, and web crawlers to jet engines, electronic switches, and other sources.

The Machine Learning Engineer builds reusable production data pipelines for implemented machine learning models, and productionalizes models in workflow environment.

Responsibilities

• Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress
• Managing available resources such as hardware, data, and personnel so that deadlines are met
• Verifying data quality, and/or ensuring it via data cleaning
• Defining the preprocessing or feature engineering to be done on a given dataset
• Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability
• Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world
• Defining validation strategies
• Training models and tuning their hyperparameters
• Supervising the data acquisition process if more data is needed
• Defining data augmentation pipelines
• Analyzing the errors of the model and designing strategies to overcome them
• Prototyping and simulating use cases for Machine learning basis and ability to operationalize into workable algorithms & solutions
• Deploying models to production
• Creating and delivering scalable production-ready machine learning workflow and solutions

Skills

• Solid background in multiple programming languages e.g. Java, Python
• Proficiency with Python and basic libraries for machine learning such as scikit-learn and pandas
• Solid background in object-oriented design
• Excellent coding practices in a continuous integration context, model evaluation, and experimental design
• Prior experience with distributed system development with micro services
• Experience/Skill of Teradata would be plus
• Prior experience with scalable infrastructure using technologies like Kubernetes, Kubeflow, Kafka, ZooKeeper, etc…would be plus
• Experience developing service-oriented systems, REST
• Strong organizational and communication skills
• Expertise in visualizing and manipulating big datasets
• Ability to select hardware to run an ML model with the required latency
• Strong organizational and communication skills
• Understanding of Machine Learning and model development framework is preferred