机器学习工程师
1.5-3万·14薪上海市本科不限经验
职位描述
ABOUT THE ROLE
Now, more than ever, Technology needs to respond quickly to turn market disruptions into opportunities for our world-class brand. To achieve this, we must continue to develop our Enterprise Analytics, Data Science & Machine Learning capabilities and team to ensure we’re maximizing the power of the Nike enterprise in the analytics/machine learning space and managing data as a competitive advantage.
If you’re ready to innovate and be a driving force for building solutions Enterprise Data and Analytics organization, come join us now! You will be part of an organization that is revolutionizing Nike technology platforms and architecting a data and analytics landscape that is simplified, modern, flexible and will ultimately enable Nike on its journey to beyond.
WHAT YOU WILL WORK ON
61 Work with business teams throughout the organization to identify complex and challenging issues to solve with data science
61 Build ML/AI prototypes in topics such as fraud detection and anomaly detection, and demonstrate their benefits to product teams and business stakeholders
61 Deploy data science powered applications in our data platform and monitor their business performances
WHO WILL YOU WORK WITH
You will be reporting to the Engineering manager, you will work with Product Manager, other Engineering Team Members and with a variety of talented Nike teammates. You will be part of team that will be a driving force in building Data and Analytic solutions for Nike Technology.
WHAT YOU BRING
61 Bachelor’s or master’s degree in computer science or related subject area with 3+ professional experience.
61 3+ years of demonstrated professional experience in Machine Learning and Data Science, with a focus on fraud detection, risk control, anomaly detection.
61 Working experience with machine learning and deep learning frameworks like Scikit-learn, TensorFlow, Keras, PyTorch.
61 Hands on experience on productize machine learning models, like model serving, model performance monitoring, model performance evaluation, models training & validation.
61 Experiences in NLP, graphical computing, graphical models, GNN will be good plus.
61 Demonstrable ability to interpret, and implement methods described in research papers in machine learning, deep learning, mathematical modeling and related fields.
61 Strong understanding of data and analytics, including experience with Big Data, real time and batch data processing is preferred.
61 Experience with cloud platforms (e.g., AWS)
61 Strong problem solving and analytical mindset
61 Effective communication skills
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