Lead Data Scientist - Gaido Health - MedTech Startup at BCG Digital Ventures
In your role as the Lead Data Scientist, you will:
- Research, design, implement and evaluate machine learning algorithms and statistical models for bioinformatics applications. Generate internal implementations to achieve results on NewCo applications
- Work closely with back/front end and iOS engineering teams to drive scalable, production-ready implementations
- Collaborate with teams across the company and serve as an internal expert on technical issues
- Document technical work as part of the product development process.
- Support patent application and scientific paper publishing process
- Contribute to our evolving cloud infrastructure, data engineering pipeline, and analysis stack
- Contribute to our data strategy design and implementation
- Identify technical challenges, define requirements and prioritize efforts
- Assist with defining requirements and architectures for next-generation machine learning / statistical analysis products
- Contribute to scientific software engineering efforts, utilizing professional coding standards and participating in reviewing PRs
- Contribute data enrichment and data monetization process
- Contribute medical devices tests and evaluation process
- Help scale data science team
As a Lead Data Scientist, You Will Have the Following Experience:
- PhD in Bioinformatics, Computer Science, Engineering, Computational Biology, Computational Physics and +4 years of relevant experience OR Master's degree (related degree above) and +8 years relevant work experience.
- Previous experience in Biotech or Life Science related field required. Work experience with medical devices and apps are preferred. Experience with medical data like EHR and EMR is a big plus.
- Advanced understanding of scientific process and analysis of empirical data. Demonstrated ability to design experimental analyses which result in meaningful conclusions. Additionally, demonstrated ability to work with experimentalists when planning physical experimental design.
- Probability distributions, classical hypothesis testing, multivariate regression, time series analysis. Ability to correctly apply probability theory/statistics across multiple domains with little or no guidance. Assist and mentor data scientists on nuances of applied statistical analysis particularly with regards to challenging problems.
- Machine Learning: classification, regression, clustering; Demonstrated ability to apply deep learning approaches to categories of machine learning problems.
- Algorithms: Fundamental data types (stacks, queues, etc.); Sorting algorithms (quicksort, mergesort, etc.); Dynamic programming
- Strong communication skills to work with stake holders, team members and other engineering team colleagues
- Proficiency with programming languages such as: Python, R and capability in writing production level code. Experience with Java is a big plus. Proficiency in ML/scientific computing libraries. Ideally some background with OOP design
- Familiarity with collaborative software engineering practices, including version control (Git), code reviews, JIRA, Confluence.
- Familiarity with cloud computing and developing cloud base APIs
- Ability to architect and implement machine learning or data science solutions with specific skills mentioned in rows above; Strong foundation in machine learning, mathematics, statistics, with demonstrated professional or academic experience
- Less than 25%