Data Scientist
ML
Confirmed live in the last 24 hours
Medical product development platform
Company Overview
Machinify is on a mission to ensure that patients get the right treatment, at the right time, at the right price. The cloud-based Machinify AI platform delivers products that are transforming healthcare administration from a human-powered, error-prone series of workflows fueled by faxes and spreadsheets to a world of transparent, realtime care and payment decisions.
AI & Machine Learning
Data & Analytics
B2B
Company Stage
N/A
Total Funding
$17.8M
Founded
2016
Headquarters
Palo Alto, California
Growth & Insights
Headcount
6 month growth
↑ 16%1 year growth
↑ 30%2 year growth
↑ 86%Locations
Palo Alto, CA, USA
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Data Structures & Algorithms
R
SQL
Python
CategoriesNew
AI & Machine Learning
Data & Analytics
Requirements
- You enjoy solving real-world business problems involving data-driven optimization and ML modeling - and have been doing that successfully for a while
- You are comfortable measuring and optimizing the direct business impact of work
- You are interested in learning about the healthcare industry and helping us improve the care millions of people receive
- You are scrappy, and love solving hard problems that matter
- You are experienced with SQL, handling large-scale data, and are comfortable with at least one programming language (Python, R, etc.)
- You have experience building ML models using modern ML approaches like Neural Nets or Tree-ensembles from scratch for new applications - making decisions relating to which supervised labels to use, the metric to optimize for, and the features likely to be useful
- You are a critical thinker who can be strategic without losing attention to detail
- You are comfortable taking the initiative and owning projects from start to finish
- You can build positive relationships based on trust and value delivered
Responsibilities
- Advance Machinify's capabilities to model and understand medical decisions which will power a range of products in the claims processing space
- Make thoughtful decisions around which methods/algorithms are likely to work well in solving the business problem at hand
- Consider the quality of the available input data and build robust systems that will work well in the presence of noise/errors
- Measure the model output in practical real-world settings and iterate your approach
- Learn about the healthcare industry and become an expert over time