Manager, Machine Learning

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