Machine Learning Scientist

Appier is a software-as-a-service (SaaS) company that uses artificial intelligence (AI) to power business decision-making. Founded in 2012 with a vision of democratizing AI, Appier’s mission is turning AI into ROI by making software intelligent. Appier now has 17 offices across APAC, Europe and U.S., and is listed on the Tokyo Stock Exchange (Ticker number: 4180). Visit www.appier.com for more information.

As a Machine Learning Scientist in Appier, you will be contributing to Appier’s research in actual use case for our products.

• →Use machine learning and analytical techniques to build prediction model for advertising/enterprise solutions.
• →Experience in analyzing and extracting valuable information from large amounts of business data
• →Design, develop and test advanced models for predictive consumer behavior
• →Co-operate with software engineering teams to drive real-time model implementations and new feature creations
• →Design efficient, scalable, automated processes for large scale data analyses, model development, model validation and model implementation
• →Research and evaluate pioneering machine learning and statistical model

[Minimum qualifications]

• Master or Ph.D. in Computer Science, Machine Learning, Math or Statistics. (Familiar with probabilistic model predictive models)
• Solid experience in system design and programming, ability to work with big data with minimal engineering support
• 3+ year experience in data mining, algorithms, statistical analysis, computational NLP and/or machine learning
• Proven ability of solving challenging problems in both academia and industry
• Extensive publication experience
• Self-motivation and an ability to priorities multiple competing challenges in a fast-growth environment
• Thinking about thinking; meta-cognition
• Applying past knowledge to new situations; remain open to continuous learning
• Thinking and communicating with clarity and precision

[Preferred qualifications]

• Excellent presentation skill to articulate product ideas to technical/non-technical audience

#LI-AK1