About
Job Description
Description
Data Scientists at Grand Rounds work on problems that are core to the company's mission. Major challenges include developing systems and models to identify the highest quality doctors in the country as well as methodologies to uncover the subtle differences between each physician's clinical expertise. Additionally, patient-level modeling allows us to understand the specific healthcare needs of every person. With a high fidelity understanding of both patients and physicians we are able to route patients to both appropriate and high quality care. In addition to developing the company's core technologies, data scientists provide decision support analysis for many teams across the organization including product development, sales, marketing, and strategy. Data scale ranges from small data sets that fit on a single laptop to large multi-terabyte clinical information in distributed database systems.
In Your First 30 days:
Onboard with the Grand Rounds team in San Francisco, setup your dev environment, get access to data systems, and become familiar with the tech stack
Learn about on-going initiatives involving data scientists, product managers, and engineers
Spend time with members of the Analytics, Medical, and Patient Care teams and learn how our teams collaborate
Become familiar with the data landscape and hit the ground running on a primary project
In Your First 60 Days:
Accelerate on-going development efforts around physician quality and expertise models
Master the ins and outs of claims data: ICDs, CPTs, and all that
Collaborate with engineers to improve the claims warehousing infrastructure
Collaborate with engineers to develop a process/pipeline for model updates that seamlessly flows data to production systems
In Your First 90 Days:
Integrate into long-term multi-data-scientist ventures and deliver on one or several short-term individual projects
Develop internal tools and codebases that are useful for other data scientists and/or engineers
Spend time with Staff Physicians and other medical domain experts to learn about the world of healthcare
Develop an understanding of both immediate business objectives as well as longer term company aspirations to develop intuition around prioritization and trade-offs between short-term deliverables and longer term R&D efforts
Responsibilities:
Develop creative solutions to diverse problems including engineering challenges, unstructured data messes, ontology development, and machine learning applicationsLead and develop major projects from end-to-end encompassing planning, design, technical implementation, debugging, roll-out to Product & Engineering, testing, and iteration
Operate at level of sophistication in statistics, machine learning, or computer science that is publication-worthy
Regularly monitor pull requests, perform code reviews, and produce excellent peer reviews on projects prior to shipping to Product & Engineering
Evaluate and experiment with new technologies and tools prior to wider adoption by the team
Work closely with analysts, data scientists, product managers, and engineers
Qualifications:
Excellent verbal communications, including the ability to clearly and concisely articulate complex concepts to both technical and non-technical collaborators
BS with 8+ years or MS with 6+ years or PhD with 3+ years of experience. Degree(s) should be in a technical discipline such as Computer Science, Engineering, Statistics, Physics, Math, quantitative social science
Work experience as an engineer highly desired
Experience with SQL relational databases as well as big data: the Hadoop ecosystem, Hive, Spark, Presto, Vertica, Greenplum, etc
Required: SQL, Python, R, linux shell scripting
Desired: Scala, Java, or Ruby
Experience with machine learning and computational statistics packages (sci-kit learn, nltk, statsmodels, networkx, gephi, arules, glmnet, bigrf, caret, igraph, MLLib, GraphX, MADlib, Weka, etc)
Experience with visualization tools (seaborn, d3, plotly, bokeh, ggplot2, rCharts, networkD3, Shiny, Tableau, CartoDB, etc)
Frequent user of cloud computing platforms such as Amazon Web Services, Microsoft Azure, or Google Cloud Platform
Bonus Points for: experience with web application frameworks (Shiny, Flask, Tkinter, Ruby on Rails, Pyramid, Django, etc)
Double Bonus Points: previous work on medical applications and/or with claims data