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 knowledgeCandidates 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 advantageExperience managing a team leading ML projects and great record delivering solutions with attention to detail and efficiencyExperience shipping Realtime models a big plusAbility to communicate effectively and establish constructive relationship with business and engineering partnersAbility to work effectively both independently and in a team environmentSkills Required
data engineering , Big Data, Machine Learning Algorithms, AI ML