Roles and Responsibilities
- • Lead and manage a team of ML scientists and ML engineers
- • Work closely with Engineering (front and back end) and ensure effective collaboration and material handoff between the ML and Engineering teams
- • Help Engineering team in pivotal decision making concerning infrastructure, architecture and scalability
- • Work closely with the Product team to understand the product aims and incorporate them in ML strategy and development
- • Communicate deep scientific concepts to Commercial and Product teams and to clients and partners
- • Be part of the leadership team to decide on strategies for short term and long term needs of the company and product roadmap
- • Maintain and scale the existing technologies and build on it
- • Keep abreast of latest developments in science, biology, medicine and ML as they relate to metabolic syndrome
- • Collaborate on study design in for Company and Company clients to feed into products
- • Strategize and execute on IP development and manage the company’s IP portfolio
- • Align the company's technology resources with the organization's short- and long-term goals
- • Serve on the executive committee to align technology goals to other departmental and organizational objectives
- • Experience working in early stage startups - from building and scaling to going through fundraising.
- • Masters or PhD in CS, EE or related computational fields (i.e. Physics, Computational Biology, Math)
- • At least 3 years of experience of working at an intersection of Machine learning, Product development and Software development teams.
- • Great communication and leadership skills and the ability to work in a fast-paced environment.
- • Practical experience with software architecture and the ability to engage with an Engineering team to help build and scale the existing infrastructure.
- • Experience working directly with front-end and back-end engineers.
- • Ability to adapt to new technologies and to learn new scientific and business concepts.
Machine Learning Qualifications
- • Extensive experience with the complete cycle of developing AI models and features from conceptualization of an idea, implementation, evaluation, and productization and delivery to the user.
- • At least 3 years of managing a team of Senior Machine Learning Scientists and Engineers.
- • Solid knowledge of analysis of multi-modal and time-variant signals
- • In-depth knowledge of start-of-the-art Machine Learning techniques and theory, particularly Deep Learning.
- • Experience building recommender systems and personalized models.
- • Good understanding of modern NLP in the wake of self-attentive transformers.
Useful to have
- • Experience with healthcare data especially time-series data
- • Experience fundraising with VCs, identifying collaborative opportunities with prospects, and defining projects with partners