Machine Learning Scientist

Our client is an American multinational financial technology company operating an online payments system in the majority of countries that support online money transfers, and serves as an electronic alternative to traditional paper methods such as checks and money orders. The company operates as a payment processor for online vendors, auction sites and many other commercial users, for which it charges a fee.

They are championing possibilities for all by making money fast, easy, and more enjoyable. Their hope is to unlock opportunities for people in their everyday lives and empower the millions of people and businesses around the world who trust, rely, and use their services.

Role Description:

• As an ML engineer, you will have the opportunity to work on large-scale ranking and recommendation systems for sequential content consumption on newly installed user interface designs 
• The solutions developed by you will aid in building novel and meaningful graph-based community assets around the network of consumers and merchants, to ultimately drive key product and marketing KPIs associated with customer experience, engagement and the revenue bottom line.

Responsibilities: 

As a Machine Learning Scientist you will be responsible for:

• Creating innovative AI/ML solutions that enhance personalization for users, with a focus on ranking and recommendation algorithms.
• Writing scalable, production-quality code to deploy models on company infrastructure, optimizing for performance and efficiency.
• Collaborating with cross-functional teams, including engineering, product, and marketing, to design, develop, and track key performance indicators (KPIs) for ranking and recommendation models.
• Conducting experiments to measure these KPIs, as well as deriving actionable insights from the data, to continually improve the technology and drive business outcomes.

Requirements:

• Advanced degree (MS or PhD) in quantitative science or engineering field (for example: Computer Science, Statistics, Mathematics, Operation Research) with a minimum of 2 years of hands-on experience for MS as an individual contributor.
• Proven expertise in designing and developing AI/ML models for ranking and recommendation systems, with in-depth understanding of both traditional collaborative/content-based recommendation methods and cutting-edge deep learning algorithms, reinforcement learning, and bandit techniques.
• Demonstrated ability to write scalable high quality code in Python, Java, Scala or a similar programming language, and to design and implement data engineering pipelines using technologies like Hive, SQL, BigQuery, or Spark.
• Proficiency in machine learning frameworks and packages, such as Tensorflow and PyTorch.