Job Description

Time Type:

Full time

At PayPal (NASDAQ: PYPL), we believe that every person has the right to participate fully in the global economy. Our mission is to democratize financial services to ensure that everyone, regardless of background or economic standing, has access to affordable, convenient, and secure products and services to take control of their financial lives.

Job Description Summary:

What you need to know about the role- You should be a driven Machine Learning Engineer.

Meet our team-
You will be joining PayPal’s nascent Personalization AI/ML, Product Intelligence & Experimentation Sciences team.

Job Description:

Your way to impact

We are seeking a talented and driven Machine Learning Scientist to join PayPal’s nascent Personalization AI/ML, Product Intelligence & Experimentation Sciences team. Ideal candidate will have a strong background in developing and implementing machine learning algorithms. We leverage state of the art machine learning techniques to solve challenging and impactful business problems in consumer product domain. This position requires the ability and curiosity to learn various advanced AI/ML techniques.

Your day to day

As a Machine Learning Scientist you will be responsible for:

• Developing state-of-the-art Machine Learning solutions to solve impactful business problems
• Working with team and stakeholders to formulate business challenges into data science and machine learning problems
• Productionalizing and automating large scale end-to-end data solutions in close collaboration with our engineering teams
• Present findings and recommendations to business partners and senior leadership

What do you need to bring-

• Masters degree or equivalent experience in a quantitative field (Computer Science, Mathematics, Statistics, Engineering, Artificial Intelligence, etc.) with 8+ yrs. of relevant industry experience
• Advanced knowledge of statistical and machine learning algorithms (e.g., logistic regression, time series analysis, random forests, SVMs, XGBoost, CNNs/RNNs)
• Experience with Graph-based algorithms and infrastructure
• Hands-on experience with popular ML frameworks and libraries (e.g., Scikit-learn, TensorFlow, PyTorch, etc.) and in dealing with big data (e.g., Hadoop, Spark , SQL, etc.)
• Ability to write scalable production-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
• Strong problem-solving skills and attention to detail
• Ability to communicate effectively and establish constructive relationship with business and engineering partners
• Ability to work effectively both independently and in a team environment
• Experience with Fintech and/or Financial Services industry an advantage

Nice to Haves:

• Experience working in Cloud based environment (e.g., GCP, Azure, AWS)
• Hands-on experience with conducting experiments in various areas of personalization and causal inferencing
• Experience working on feed-based ML ranking and recommendation systems