We are a team of technologists, software engineers, doctors, nurses, and lean healthcare experts with on-the-ground experience solving operational challenges in clinical environments. Qventus helps hospitals like Stanford/Mayo Clinic/Emory/New York Presbyterian reduce wait time for patients, prevent patient falls, and generally helps hospitals deliver a better and safer experience for patients, resulting in reduced injuries, loss of life, and financial waste.
We're making a big difference. Qventus has meaningfully affected patient experiences and outcomes for almost 4 million patients across the united states. Our platform works with hospital data to predict negative outcomes for patients and associated potentially disastrous situations that are likely to occur, then applies our system of action to reach the right doctors and nurses, at the right time, telling them where to go to stop these events before they happen. Think Minority Report applied to hospitals.
Our founding team has done this before. Two of our three founders worked in consulting for over a decade solving efficiency problems in hospitals. They know hospitals, where they've been, where they want to go, and how to work within them to drive the necessary mind shift and change management we need to drive this new wave of technology.
Our team is top notch! We have the fortune of having amazing engineerings and data scientists from top schools (Stanford, MIT, Cal, etc.) applying the latest in artificial intelligence and machine learning to create products that help healthcare organizations around the world and their people adapt in the moment and make the right decisions from the most complex data. Furthermore, we employ several practicing clinicians (practicing surgeons, doctors, nurses) in the company to help us understand the hospital environment. We take what we do very seriously.
Skills: MySQL, Python, SQL, Natural Language Processing, Data Modeling, Data Analytics
Have you ever found yourself or a loved one waiting hours and hours in a hospital Emergency Room to get care? Or have you ever had a surgery scheduled for months in the future that needed to happen sooner? Unfortunately, our healthcare system is full of these types of operational problems. Our work saves lives and helps hospitals cut tens of millions of dollars in operational costs, while improving the quality of care they’re able to deliver.
Qventus is a real-time decision making platform for hospital operations. Our mission is to simplify how healthcare operates, so that hospitals and caregivers can focus on delivering the best possible care to patients. We use artificial intelligence and machine learning to create products that help nurses, doctors, and hospital staff anticipate issues and make operational decisions proactively.
Qventus works with leading public, academic and community hospitals across the United States. The company was recognized by the 2019 Black Book Awards in healthcare for patient flow and by CB Insights as a 2019 top 100 Most Promising Company in Artificial Intelligence. Recently, Qventus won the Robert Wood Johnson Foundation Emergency Response for the Healthcare System Innovation Challenge through its work helping health systems across the country plan for and operate in the COVID pandemic.
Qventus is looking for Data Scientists to help build the next generation of Qventus’s AI. You’ll join a cross-functional team of clinicians, data scientists, data platformers, and product experts to help care teams across the country make day to day decisions to get patients the right care faster and with less overhead.
As a data scientist at Qventus, you will have the opportunity to explore Qventus’s unique and rich Healthcare dataset to develop and deploy cutting edge ML based solutions. You will evaluate potential modeling approaches, build features together with data platform partners, implement algorithms, and drive meaningful improvements to our underlying ML infrastructure to scale Qventus into the next generation. You should be strongly motivated to have an impact in the company and dedicated to helping improve the quality of healthcare operations.
This role is remote first, but we have offices in Mountain View, CA and open to local hires as well.
- Drive the development of our machine learning platform to efficiently train, evaluate, and deploy high quality models.
- Develop, deploy, and tune performant and highly scalable machine learning models in the healthcare space strategically employing a wide array of modeling and statistical techniques.
- Collaborate with Product partners to experiment, design and measure quality of model based interventions including development of analytics dashboards.
- Develop tools and resources to improve transparency into Data Science technical architecture and increase collaboration with engineering and analytics partners.
- Proven ability to develop and tailor algorithmic solutions to business problems in collaboration with product or delivery partners.
- 3+ years industry experience developing, launching, and iterating on machine learning models and/or developing the core data science platform.
- High competency in Python, with experience developing scalable systems and using statistical packages such as Pandas, Scikit-learn, and XGBoost.
- Strong software development foundations - dedication to high code quality, stable architecture and an eye towards maintainability.
- Excellent SQL - hands on experience manipulating data sets, data cleaning, and pipelines Interest and ability in learning and working in a fast paced dynamic environment across multiple technologies.
Nice to have's:
- Strong cross-functional communication - ability to break down complex technical components for technical and non-technical partners alike
- Demonstrated understanding of a wide variety of statistical and machine learning methods (resampling, regression, classification, ensemble methods, transfer learning, etc).
Practical hands on experience with:
- Natural Language Processing or Understanding techniques and productionalization
- Deep Learning modeling techniques and infrastructure
- Ecosystem of data platform technologies such as: AWS Services (RDS, ECS/EKS, Lambda, S3), DBT, Snowflake, Databricks
- Data analytics tools (Looker preferred)
- Experience in healthcare working with real world data, particularly in the inpatient setting.
- Bachelor’s degree in Computer Science, Engineering, or related field, or equivalent training / experience
Qventus is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances.
Candidate information will be treated in accordance with our candidate privacy notice which can be found here: https://qventus.com/ccpa-privacy-notice/
Engineering is comprised of several teams: - Web Apps: Responsible for our web platform and associated services. - Mobile: Responsible for our mobile products (iOS and Android). - Data Platform: Responsible for ETL, data processing and platform, managing data lifecyle from customer ingest to persistence, serving data to data science and web apps groups. - Data Science: Responsible for our ML development. - DevOps: Infrastructure and associated infrastructure services, working with teams to build and maintain CI/CD pipelines. - QA: Responsible for quality definition, automation, QA infrastructure, driving quality process from development through release. Functional QA is largely outsourced, we prefer to have QA team focused more on the engineering required to scale a large QA org.
Some of the challenges we are facing:
Using AI/ML- We use predictive analysis to help doctors and nurses make proactive operational decisions so they can focus on patient care
Data scaling- We are starting to get significantly larger volumes of data, which is also increasingly becoming more and more clinical (lab results, etc) in nature.
3)Data sensitivity- Because this data is more clinical, it is more sensitive and requires a higher bar on how we set up and monitor our systems.
4)Data Ingestion- Getting data out of hospitals is not an easy task. We have to figure out more scalable solutions to help us grow quickly. This is an industry wide problem and we are looking to do it better than just about anyone else.
5)Product growth- Expansion in more areas of the hospital means we have more to learn and an opportunity to expand our product to achieve more outcomes.