Senior Data Scientist

Summary: 

The Senior Data Scientist, will be a technical individual contributor at Palmos Labs, working with multiple sources of data to develop analytical predictive models that support clinical decision making. The ideal candidate has deep expertise in healthcare data and signal processing with experience working in a regulated environment.  

Duties and Responsibilities: 

He/she will work as part of the Palmos Labs cross-functional team in the research and development of data modelling for Palmos and are accountable for all code, specifications, data integrations and quality checks to support the Palmos Labs portfolio. He/she will be responsible for the data flows into the Palmos Labs data lake from ongoing projects and studies across the Palmos Labs portfolio. He/she will work independently on multiple internal and co-sponsored projects in parallel. While this is not a customer-facing role, interaction with customers will be required, particularly in beta-testing, so he/she should have strong communication skills.  

Specifically, our ideal candidate is someone that we can trust to make good decisions when modelling and running applied data analysis (idea/requirement -> gather data -> candidate models -> evaluation -> production -> deployed). This is a mixture of knowledge, experience, and attitude.  

1. Knowledge: you have moderate statistical skills and can understand the assumptions (limitations) of the models you’re evaluating. 

2. Experience: you have done the whole pipeline above, ideally more than once. You understand how to interpret and troubleshoot models in production. You are able to work around business constraints. Also, you MUST be a competent coder. We’re looking for candidates who prioritize code quality and correctness. Whether it’s writing a data transformation or training a model, we expect you to take ownership of what you write (with or without AI). 

3 Attitude: The candidate needs to be curious and a problem solver and have a “yes, and” perspective. 

Wishlist for this role specifically: 

1. Experience working with regulated medical data 

2. Experience with signals data, and/or experience with real world healthcare data 

3. Substantial experience with time-series modeling and survival analysis. (Hidden Markov Models, ARIMA models, Survival Analysis) 

4. Experience with standard classification methods (logistic regression, boosting models [XGBoost/GBMs/etc]) 

5. Familiarity with Bayesian methods.  

6. Competent python programmer (ability to code outside of notebooks, familiarity with pydantic, pandas and/or polars, etc.) 

7. Experience deploying models and managing deployed models (data drift, testing strategies, basics of APIs for models) 

Position Level & Compensation: 

Competitive salary range  

Includes equity participation and competitive benefits package 

Technical Qualifications (Minimum Required): 

A Master of Science (M.S.) degree or academic equivalent in a discipline commensurate with the specialty, such as Computer Science, Biology, Data Science, or a related quantitative field.  

5+ years of extensive proficiency in the Python programming language for advanced data analysis and machine learning. Additional experience with R, MATLAB, C/C++ preferred. 

3+ years of demonstrated experience in developing and deploying machine learning models into a production environment. Advanced signal processing expertise (digital filters, spectral analysis, time-frequency analysis) 

Experience with signal processing including instrumentation, sensors and data acquisition, and its application to real-world problems. 

Preferred Qualifications: 

Experience working with healthcare data. 

A strong publication record in peer-reviewed journals or conferences in the areas of applied data analysis, machine learning, or signal processing. 

Understanding of healthcare data privacy regulations 

Demonstrated ability to work independently and collaboratively in a fast-paced cross functional environment and be curious and to be open to feedback and to act on the feedback.  

Skills and Competencies: 

Proven ability to rapidly learn new concepts and apply to new and diverse domains.  

Excellent communication skills with the ability to articulate complex technical concepts to both technical and non-technical stakeholders.