Machine Learning Architect

**Let’s talk about the team**:
The Data Science and AI/ML team collaborates across the organization to identify, develop and deliver Artificial Intelligence (AI) and Machine Learning (ML) powered software solutions that improve patient outcomes, delight our partners and customers, and improve the way we do business in an “AI First” fashion. On any given day, the team could be working on building sophisticated models to delivering personalized recommendations to patients to improve their sleep, identify optimal equipment and settings using our unparalleled store of billion+ nights of sleep data, proactively identify other health risk factors, or help optimize complex, global supply chain operations.

**Let's talk about the role**:

- Drive a culture of learning on your team that challenges the status quo.
- Be a mentor and technical leader for a talented team of engineers, challenge them and help them grow
- Facilitate and drive key technology decisions with senior technology stakeholders
- Be committed to growing and mentoring talent with the goal of building a world-class development culture.
- Work closely with stakeholders from Product Management, Engineering, and other business stakeholders to create impactful, intelligent AI/ML features and products.
- Lead and develop team of ML engineers to enable the AI/ML roadmap for the organization.
- Develop and maintain the appropriate departmental processes to optimize team performance and productivity.
- Lead AI/ML model development, deployment, implementation, integration and monitoring.
- Collaborate closely with other team members including Data Scientists and Data Engineers and “own” the end-to-end process, train and mentor junior team members
- Build and maintain optimal global AI/ML architectures and provide production support to live AI/ML products
- Thoroughly document the model & ML system design, experiments, tests, validations, and live metrics and outcomes, typically on Confluence. You may be asked to write documents for use in the preparation of intellectual property and technical publications.
- Stay informed of industry trends and enable successful AI/ML solutions by leveraging best practices.
- Identify, design, and implement internal process improvements: Automating manual processes, re-designing infrastructure for greater scalability, etc.
- Build capabilities needed for production-level AI/ML systems, such as data processing, model inference, automated re-training etc.
- Work with stakeholders including the Executive, Product, Data and Design teams to help with AI/ML-related technical issues and support their AI/ML infrastructure needs.
- Actively handle escalated incidents to resolution and suggest solutions to limit future exposure.
- Participate in Code Review and process improvement.

**Let's talk about you**:

- 8+ years’ industry experience in Machine Learning Engineering, Data Science, or Data Engineering
- M.S or PhD in Data Science/Machine Learning or closely related areas such as Computer Science, Operations Research, Applied Statistics, and Biomedical Informatics.
- Experience managing Data Science, Machine Learning and/or AI teams
- Strong experience leading AI/ML programs, developing development processes and mentoring junior team members
- Good foundation with development of data analytics systems, including data exploration/crawling, feature engineering, model building, performance evaluation, and online deployment of models.
- Experience building scalable AI/ML systems for continuous training automation, computer vision, natural language processing, recommender system or similarly advanced AI/ML problems.
- Experience in handling large and distributed datasets on Spark, Hive, relational SQL and NoSQL databases.
- Experience with AWS or other cloud-based tools and technologies for data pipelining, model development, deployment, monitoring, and MLOps tools including SageMaker, Airflow, Kubeflow, etc
- Rigorous academic or experiential knowledge of the mathematical essentials for Data Science, including key concepts in probability and statistics, optimization, time series analysis, linear algebra and discrete math. Sampling and estimation, Bayesian analysis, hypothesis testing, uncertainty estimation, stochastic methods, and graphical methods are particularly important to know.
- Experience with machine learning techniques including regression methods (linear, logistic, lasso, support vector, etc.), classification (tree-based models such as XGBoost and Random Forest, Neural Networks, Deep Learning - CNN, RNN, LSTM, etc.), as well as knowledge of clustering and unsupervised learning, time series forecasting and optimization methods.