Machine Learning Engineer

Collaborate & Innovate – Work closely with cross-functional teams to identify opportunities, gather requirements, and transform challenges into scalable technical solutions. Design Advanced Models – Develop state-of-the-art data science and machine learning approaches, leveraging proven methodologies or creating custom algorithms tailored to business needs. Build Robust Systems – Partner with data engineers and platform architects to implement high-performance, real-time and batch decisioning solutions. Communicate Insights – Present findings to business stakeholders and executives, ensuring data-driven strategies translate into meaningful business outcomes. Explore & Evolve – Stay at the forefront of technological advancements by researching new techniques across data science, engineering, and visualization to continuously refine the team’s capabilities. Minimum Qualifications Communication & Presentation: Superior verbal and written communication and presentation skills, ability to convey meticulous mathematical concepts and considerations to non-experts. Machine Learning: Understanding of common machine/deep learning algorithms and practical experience in one or more of the following areas: time series forecasting, anomaly detection, convex optimization, computer vision, NLP, LLM, recommendation system and Auto ML Databases: Solid understanding of relational databases, including SQL, and large-scale distributed systems such as Hadoop and Spark Programming language: Ability to implement data science pipelines and applications in a general programming language such as Python, Scala, or Java Preferred Qualifications Insight: Ability to extract meaningful business insights from data and identify the stories behind the patterns Creativity: to engineer novel features and signals, and to push beyond current tools and approaches Bilingual: Excellent verbal and written communication skills, in both Mandarin Chinese and English