Machine Learning Director
What You Will Work On
Equip yourself with a bold, ambitious tech startup mentality, you will be driving the overall strategy, priorities, execution, and engineering capabilities for Global AiRBots team. This team uses machine learning techniques to implement mechanisms to detect, prevent, and respond to resellers, bad actors, and bots that directly impact our bottom line by infiltrating company’s digital ecosystem. With teammates in Portland, Boston, China, and Poland, you will join a global organization working to tackle machine learning problems at scale. You will lead the team from the ground up to craft and implement scalable applications that bring to bear AI/ML models; with a focus on combating digital attacks that result in incredible measurable business and brand impact. You will build, grow and inspire highly skilled, cross-functional engineering teams that deliver end-to-end AI/ML capabilities for Global. We are looking for candidates who embrace and embody company's core values (maxims) in your work and interactions with peers, stakeholders, and direct reports. You will provide leadership and direction in the innovation of ground-breaking Bot detection and analytical-based solution with a focus on data science and applied ML techniques.
What You Bring
1. Bachelors, Masters, or PhD degree in Computer Science, Software Engineering, Information Systems or equivalent work experience.
2. 8-10+ years of professional experience with a combined background of AI/ML, data analytics, and software engineering.
3. 5+ years of team management experience and building effective tech/engineering teams
4. Proven technical leader with deep knowledge and experience in creating production grade, end-to-end data and AI services at scale.
5. Knowledgeable in deliberating modern data architectures, distributed database, CAP theorem, privacy compliance, low latency APIs and microservices.
6. Able to deep-dive into code and architecture to provide an informed opinion on benefits/trade-offs of different design choices.
7. Experience with platform development for providing foundational stream processing, data engineering, data warehouse, data access, data servicing, AI/ML (i.e. Kafka, Flink, Spark, Presto, Dynamo, Snowflake, Apache Ranger, HBase, etc.)
8. Knowledgeable in software engineering, especially modern cloud computing stacks for deploying machine learning and microservices at scale (i.e. AWS for cloud provider, EKS for Kubernetes, Docker for containers, Jenkins for CI/CD, Kafka/Spark/Nifi/Kubeflow/ Tensorflow/Keras/Pytorch for deep learning framework, etc.
9. Working experience with Python, Pyspark, Machine learning (Supervised & Unsupervised) and deep learning frameworks like Scikit-learn, TensorFlow, Keras, PyTorch
10. Experience in mining features from Clickstream data, CDN and various big data sources to support anomaly detection/fraud related use cases is highly desired
11. Hands on experience with MLOps frameworks such as TensorFlow Extended, Kubeflow, Airflow or other workflow engines, Model Serving (Real time/batch) and distributed training.
12. Experience applying data science techniques to the cyber security problem set, and domains like Natural Language Processing (NLP), Bayesian models, graphical models, GNN, GAN and a variety of neural network approaches
13. Highly organized and able to manage multiple timelines, priorities, and teams with ease. At the same time, able to quickly flex and pivot due to changing circumstances which present new opportunities for Nike.
14. An effective communicator who can seamlessly transition between discussing high level vision with executives to low level tactics with the dev teams.
15. Able to influence and align efforts across the company to act as one in pursuit of a common goal.
16. Experience working with multiple Agile teams
17. Experience with Agile methodologies such as Scrum, LEAN, Kanban, XP Programming etc.
18. Previous experience and successful track-record of learning new tools and technologies
