Director, Data Science and Machine Learning

MedArrive is a fast-paced and fast-growing start up on a simple mission: Improving people's lives by bringing more humanity to healthcare. Our enemy is an often soulless, transactional healthcare system that’s increasingly engineering the vital human touch away from the experience - and often hard to access.

We are looking for a seasoned data scientist to catalyze a data-driven product and service culture at MedArrive.

As our Director of Data Science and Machine Learning, you will own the data and analytics substrate that forms the intelligence layer powering our entire platform. You will do so balancing the need to set up a framework that scales to our future ambitions while meeting our more immediate market and customer needs. Reporting to the CTO, you will be responsible for  nurturing and building out a high performing remote data science and machine learning function, including data, ML, and analytics engineers as well as data scientists. You will work with amazing colleagues, brainstorm new ideas, and develop data processing techniques, models,  algorithms and visualizations to solve challenging problems and communicate insights that have a substantial impact.

Our goal is to create a platform and eventual marketplace for in-home medical support in a complex, multi-stakeholder industry. Our platform and apps are deployed in AWS, with our data infrastructure largely built out of DataBricks using Scala, PySpark, and Python. Our intelligence layer - an extension of our data infrastructure - leverages both predictive and prescriptive analytics techniques to orchestrate actions across patient journeys and applications. Our end-users are internal Cx & Clinical staff, consumers/patients, field providers (EMTs, Paramedics, etc.) and staff at healthcare organizations (hospitals, health plans).

What you'll do:

• Ongoing stewardship of MedArrive’s Technology Strategy and Plans
• Partner with the rest of the platform leadership team to ensure the overall technology roadmap and backlog supports business direction and priorities.
• Forge the long term vision and direction for the MedArrive Intelligence Layer (underlying data, analytics & services that power the platform)
• Own planning, design and execution to keep moving forward towards a scalable data & analytics platform and efficient data pipelines in MedArrive’s AWS Footprint.
• Drive strong execution against roadmap
• Partner with leaders and stakeholders across the business to investigate, prioritize, and deliver impactful data products, machine learning models, and analyses.
• Develop, collect and maintain structured and unstructured data sets for analysis and reporting. As needed, Identify and apply methods to acquire, explore, cleanse, and fuse data from different sources.
• Continually monitor and adjust as needed to balance between long term and near term needs, tech debt and new capabilities, while inspiring the team to bring their best selves to and be engaged in their work at MedArrive.
• As needed, directly handle high-impact projects.
• Understand and manage stakeholder expectations within the team and across the organization
• Solidify and scale the MedArrive Data Science & Engineering Culture and Practice: 
• Manage the development lifecycle, using data to drive continuous improvements, with a strong focus on meeting the team’s commitments given evolving business priorities and market/ customer needs.
• Lead definition and implementation of competency definitions at every level, and align with recruiting initiatives.
• Drive ongoing improvement in functional excellence and best practices (design sessions, code reviews, automation, etc.)
• Above all, lead the team responsible for this Platform, evolving it into a high-performing team of Data Scientists & Machine Learning Engineers over time.

What you'll need:

• 7+ years of hands-on experience in a Data Science/Engineering or Analytics role at commercial technology or technology-enabled services companies.
• Bachelor’s Degree in Mathematics, Computer Science, Economics (or related field) 
• Demonstrated expertise with data science methods, multivariate statistical modeling, neural network architectures, machine learning algorithms and production level programming principles. Experience crafting, conducting, analyzing, and interpreting experiments and investigations.
• Demonstrated ability to apply programming skills to data acquisition, preprocessing, modeling and monitoring as well as proficiency with common data science/analytics software languages  & tools (e.g. Python, SQL) and cloud computing platforms (e.g. Amazon Web Services).