Senior Data Scientist

JobgetherHealthcare AI

Based in CanadaFull-TimeSenior

Salary not disclosed

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Job Details

Experience: 3+ years of industry experience developing, launching, and maintaining machine learning systems in production at scale; at least 1 year of experience developing and tuning LLM systems.
Required Skills: AWSPythonSQLMachine LearningSoftware EngineeringDatabricksNLPLLM

Have 3+ years of industry experience developing, launching, and maintaining machine learning systems in production at scale.
Have at least 1 year of experience developing and tuning LLM systems.
Demonstrate strong programming skills in Python and SQL.
Have hands-on experience manipulating datasets, cleaning data, and building data pipelines.
Have solid software engineering foundations, with a focus on code quality, maintainability, and stable architecture.
Demonstrate the ability to collaborate with technical and non-technical stakeholders across functions.
Have strong analytical and problem-solving skills, including the ability to evaluate modeling approaches and translate findings into practical solutions.
Experience with healthcare data or hospital operations is a plus.
Experience with Databricks, AWS, dbt, or similar data and cloud platforms is a plus.
A master’s degree or equivalent professional experience in Computer Science, Statistics, or a related field is a plus.

Build, refine, deploy, and maintain statistical and machine learning models at scale, including NLP and explainable AI approaches.
Develop, tune, and operationalize LLM-based systems.
Establish evaluation methods for LLM performance, reliability, and user trust.
Evaluate modeling approaches and contribute to continuous improvement of ML and LLM solutions.
Design experiments and analytics frameworks to measure the real-world impact of AI solutions in healthcare environments.
Clean, transform, and analyze data, and develop pipelines using Python and SQL.
Partner with clinicians, product teams, data scientists, and data platform engineers to translate insights and models into production-ready products.
Communicate technical findings, analytical results, and model performance to technical and non-technical stakeholders.

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