Senior Data Scientist
New
Based in the United StatesFull-TimeSenior
SalaryCompetitive salary with performance-based compensation opportunities.
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Job Details
- Experience
- 5+ years
- Required Skills
- AWSPythonSQLFlaskGitMachine LearningData scienceFastAPI
Requirements
- Bachelor’s degree in Data Science, Statistics, Computer Science, Mathematics, or a related quantitative field; Master’s or PhD preferred.
- 5+ years of professional experience in data science, machine learning, or applied analytics roles.
- Strong programming skills in Python with experience building and deploying production-ready ML models.
- Experience developing APIs for model serving using frameworks such as Flask or FastAPI.
- Strong understanding of statistical methods, probability distributions, hypothesis testing, and data transformations.
- Hands-on experience with supervised and unsupervised learning techniques, including tree-based models, clustering, and regression methods.
- Experience working with AWS cloud services such as SageMaker, Athena, RDS, Glue, and related tooling.
- Strong SQL skills with the ability to query, transform, and analyze large datasets efficiently.
- Experience with version control systems such as Git and collaborative development workflows.
- Ability to communicate complex technical concepts clearly to both technical and non-technical audiences.
- Customer-focused mindset with experience presenting insights and recommendations to external stakeholders.
Responsibilities
- Develop, train, validate, and deploy machine learning models to support healthcare-focused products, including applications in medication intelligence, risk monitoring, and operational optimization.
- Translate ambiguous business problems into well-defined analytical questions, hypotheses, and data science solutions.
- Design and implement end-to-end data science workflows, including feature engineering, model training, evaluation, and productionization via APIs.
- Define, track, and monitor KPIs to evaluate model performance, business impact, and long-term value delivery.
- Collaborate closely with product managers and stakeholders to scope opportunities, build prototypes, and refine solution requirements.
- Present insights, model outcomes, and recommendations to both technical and non-technical audiences, including customers and executives.
- Partner with engineering teams to deploy scalable ML solutions using cloud infrastructure and production-grade systems.
- Continuously analyze healthcare datasets and adapt models as new data sources, regulations, and business needs evolve.
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