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Job information

$ 4,000 - 8,000 USD per month

Job Description

Responsibilities:

  • Work cross-functionally with business managers/product managers/engineers, and designers
  • Develop predictive analytics for customer behaviour and apply machine learning, optimisation techniques, in core subject areas including but not limited to: fraud detection, recommender systems, product understanding, customer segmentation, demand forecasting and supply chain optimisation.
  • Build, validate, test, and deploy models and algorithms. Able to implement data science experimental framework to enable organization-wide experiments
  • Make strategic data architecture recommendations
  • Visualize data for business and technical audiences and Conduct end-to-end data management starting from data collection / preparation to providing insights for key stakeholders
  • Effectively communicate solution approaches and analyses to stakeholders.
  • Contribute to team’s innovation and IP creation.

Knowledge:

  • Supervised/unsupervised learning, Classifier algorithms, clustering algorithms, data engineering, feature engineering/optimization
  • Knowledges on recommendation system, knowledge graph, nature language processing, image processing and deep learning.
  • Strong data visualization capabilities
  • A/B testing, bandit optimization, experiment design

Requirements:

  • Minimum B.S. degree in Computer Science or a related technical field. Masters or PhD in Computer Science, Statistics, Biostatistics or fields related to data mining preferred
  • Excellent communication skills with the ability to identify and communicate data driven insights:
  • Detail-oriented and efficient time manager who thrives in a dynamic and fast-paced working environment
  • 2+ years of Python and/or R development and Unix/Linux system experience
  • 2+ years of SQL (Mysql, Mssql, PostgresQL, Hive, etc) experience

Preference will be given to candidates with the following additional requirements as below:

  • Working experience on big data analytics and distributed databases or distributed systems (Hadoop, Spark, Hbase, Cassandra etc.)
  • Working experience with parallel algorithms in data modeling / machine learning
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