We’re looking for a Machine Learning Engineer to bring our Machine Learning production capability to the next level!
The ideal candidate will have a passion for building end-to-end applications on top of the analytics models designed by data scientists to bring out values to the business and our customers.
Responsibilities:
Rapidly architect, design, prototype, and implement architectures to tackle the Big Data and Data Science needs for a variety of Fortune 500 corporations and other major organizations.
Work in cross-disciplinary teams with SK experts to understand client needs and ingest rich data sources such as social media, news, internal/external documents, emails financial data, and operational data.
Research, experiment, and utilize leading Big Data methodologies, such as Hadoop, Spark, Redshift, Netezza, SAP HANA, and Microsoft Azure.
Translate advanced business analytics problems into technical approaches that yield actionable recommendations, across multiple, diverse domains; communicate results and educate others through design and build of insightful visualizations, reports, and presentations.
Develop skills in business requirement capture and translation, hypothesis-driven consulting, work stream and project management, and client relationship development.
Qualifications & experience
Bachelor’s degree from an accredited college/university in Computer Science, Computer Engineering, or related field
Minimum four years of big data experience with multiple programming languages and technologies;
Master’s with 2 years of relevant experience; or PhD with 1 year of relevant experience.
At least 3 years of experience with building scalable Machine Learning platform/system in Spark/Scala/Java etc.
Understanding of Machine Learning Theory (e.g. Deep Learning/Time Series Modeling/Anomaly Detection/Clustering/Predictive Modeling, etc.)
Hands-on experience with various open source tools (e.g. Tensorflow, Pytorch, XGBoost, etc.)
The capability to quickly implement cutting-edge machine learning research is a big plus.
Strong problem-solving skills and capable of working with scientists, analysts, and technical software developers.
Domain knowledge of machine learning and computer vision techniques for classification, detection, and key attribute extraction.
