Machine Learning Manager
Remote - U.S.
We are looking for an experienced engineering manager with outstanding expertise in applied Machine Learning (ML) and Natural Language Processing (NLP) to lead the ML team in delivering on our team's mission of expanded quality and scalability at Ginger. The ML team uses our large and unique datasets to model, understand, and predict emotional and cognitive state, care quality, factors driving optimal well-being and many other essential signals.
Machine learning (ML) in the Data Science team moves the needle for Ginger by building two essential types of magic:
- Products developed, launched and supported which drive scalability, introduce science-fiction-like efficiency and increase quality of care to superhuman levels previously unseen.
- Deep multi-dimensional actionable understanding of our members, caregivers, and treatments unlocked in the form of features generated, stored and surfaced by our ever-growing state of the art AI engine.
As our Machine Learning Manager, you will run ML at Ginger, leading our innovative team to algorithmically leverage the many dimensions of our data to better serve our members, caregivers and internal teams. Partnering closely with both our caregiver and member app product teams, you'll work synergistically to ideate projects and deliver value to our customers through the use of applied machine learning. You will also lead discussions within your team and the whole DS team to identify new and large opportunities that will expand our capabilities. We are looking for someone who will thrive in a fast-paced environment and can be hands-on when needed.
This role is a part of the Data Science team and will report to the Director of Data Science.
What you'll do
As a leader of the machine learning team you’ll:
- Define technical vision and strategy for machine learning at Ginger. Build, own and iteratively update the long-term ML roadmap, planning key quarterly deliverables which are high-impact and achievable. Define key metrics and thresholds of success at multiple levels from algorithm to team.
- Stay on top of the state of the art, identify new opportunities, and lead the team to innovate in areas such as:
- Natural Language Processing applied to behavioral health conversations
- Time series forecasting using multiple heterogeneous data sources
- Therapeutic content recommendation systems
- Scope solutions for your teams which maximize performance while minimizing complexity, taking advantage of existing state of the art technologies where possible and innovating when needed.
- Scale up our impact by recruiting and integrating high-caliber talent into an already incredible team (size of 4, with significant growth scheduled for this year)
- Mentor and develop promising ML engineers technically and professionally into exceptional talent, diving deeply to provide coaching and hands-on technical guidance that ensures quality of their code and their ultimate success.
- Instill and upkeep a culture of collaboration, rigor, curiosity and fun.
- Own our systems, environments and processes to ensure we have minimal friction throughout.
- Work cross-functionally with product, design, care, and engineering teams to scope initiatives and prioritize efforts that will bring your team’s magic to life.
- Influence product strategy through specialized knowledge mixed with a vision of expanded quality and efficiency in everything Ginger does.
- Effectively communicate your team’s work with senior leadership, achieve buy-in from partners, and align on staffing needs
- Bring robust efficient state-of-the-art models to production; design and build infrastructure and systems for monitoring model inputs and feature outputs, tracking quality, usage, and consistency.
- Spearhead efforts to develop world-class AI products in real-time and at scale, including:
- Health forecasting from sleep and activity trends
- Information retrieval
- Conversational AI
- 7+ years of industry experience using ML to solve real-world problems with large datasets
- 3+ years of experience as a tech lead or beyond of team size 3 or greater.
- 2+ years as an Engineering Manager, successfully managing and scaling teams of 3+ engineers with strong communication, coaching, and prioritization skills as well as high EQ.
- Strong project management skills for planning and executing complex projects with multiple contributors, coordinating with several other teams.
- Focused first on efficient shipping of effective solutions, with secondary focus on research and pushing the state of the art.
- Demonstrated ability to triage and solve open-ended, ambiguous systems-level sets of problems.
- Ability to attract, hire, and coach world-class engineers. Can gain trust of the team and guide their careers.
- Strong programming skills in Python and fluency in data manipulation (SQL, Spark, Pandas), machine learning (scikit-learn, XGBoost), deep learning (PyTorch, TensorFlow) and NLP (Spacy, HuggingFace, large language models like BERT) tools. Ability to write production code as needed.
- Recent experience building, deploying, and managing production ML systems and data-driven products at scale
- Strong statistical and mathematical intuition with an appreciation for concepts such as causality, selection bias, incrementality, hypothesis testing, etc.
- Self motivated and passionate to lead your team, encourage best practices, and constantly improve.
- Value collaboration, transparency, and psychological safety. You lead by example, embodying these important aspects of success.
- Passionate about behavioral health technology and ready to realize its impact on the world
- People focused, inspiring, and fun!
- BS, MS or PhD in CS, Math, Applied Sciences and Engineering, or equivalent real-world experience
- Experience in human data annotation (including active learning), tracking inter-annotator agreement and ensuring quality of resulting models
- Skilled building and architecting large-scale, production quality backend systems, especially in applied machine learning or data pipeline/warehousing
- Strong understanding of privacy and its implications for modern ML systems.
- Experience with modern ML techniques and systems like data augmentation, feature stores and federated learning.
- Advanced physiological and actigraphy-based signal understanding and integration.
- Experience in the healthcare space
Some technologies we use
- Python (and it's NLP, ML, data analysis and visualization libraries)
- AWS SageMaker (exploration, training and production endpoints)
- Looker (BI tool where we surface data to stakeholders)