What you need to know about the role-

This role needs to have a strong background in Machine Learning, practical experience in building and implementing large scale predictive models to solve business problems, bringing insights and identifying additional opportunities from Data & Machine Learning to market, and experience working with data science teams.

Meet our team-

PayPal's Global Data Science team is looking for a Machine learning Scientist to help us develop and enhance machine learning capabilities to innovate and improve our Payment KPIs and platform.

Job Description:

Your way to impact

This role helps to solve business problems, bringing insights and identifying additional opportunities from Data & Machine Learning to market, and experience working with data science teams.

Your day to day

In your day to day role you will -

• Design, develop and implement data-driven strategies and AI/ML model for transaction expense reduction, Improving Authorization rate, platform Availability and reduce latency 
• Create innovative features and data for payment model, execute and deliver impact, align with stakeholders and implement these capabilities 
• Communicate analysis results and complex concepts in a clear and effective manner 
• Collaborate with other ML scientists, product managers and engineers to formulate innovative ideas, and test / implement through advanced data science technique 
• Lead multiple projects focusing on impact as well as mentor junior members of the data science team 

What do you need to bring-

• Master's degree or equivalent experience in a quantitative field (Computer Science, Mathematics, Statistics, Engineering, Artificial Intelligence, etc.)
• 10-15 total years of experience in IT and 6+ yrs. of relevant industry experience with demonstrable skill and aptitude for navigating ambiguity by research and applying core ML knowledge 
• Candidates should have in-depth knowledge of machine learning algorithms, explainable AI methods, neural networks, logistics/linear regression, tree-based methods and NLP.
• Demonstrated record of building and deploying end-to-end ML solutions in a production environment.
• Ability to write scalable production-quality code in Python and to design and implement data engineering pipelines using technologies like SQL, BigQuery, or Spark etc. 
• Hands-on experience with popular ML frameworks and packages such as TensorFlow and PyTorch. GCP/Hadoop and big data experience – an advantage 
• Experience managing a team leading ML projects and great record delivering solutions with attention to detail and efficiency 
• Experience shipping Realtime models a big plus 
• Ability to communicate effectively and establish constructive relationship with business and engineering partners 
• Ability to work effectively both independently and in a team environment