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Position: Data Scientist
Location: Remote
About The Role
- We re looking for a forward-thinking Data Scientist II who thrives at the intersection of analytics, technology, and strategic collaboration. This role isn t just about modeling data it s about shaping smarter, people-first solutions that improve operational efficiency and care delivery across the healthcare landscape.
- You ll partner across departments to turn complex data into actionable insights, using machine learning, statistical modeling, and a strong ethical compass to drive innovation and business impact.
- Bachelor s degree in Computer Science, Data Science, Statistics, or a related field with at least 4 years of industry experience, or a Master s degree with 2+ years of relevant experience.
- Hands-on experience with Python or R, along with machine learning libraries such as TensorFlow, PyTorch, Keras, or Spark.
- Proficiency in applying statistical methods and data analysis techniques.
- Comfort working with relational (SQL) and non-relational (NoSQL) databases.
- Strong problem-solving instincts, with a logical and detail-oriented mindset.
- Excellent written and spoken communication skills capable of translating complex findings into actionable business language.
- Familiarity with cloud-based platforms and experience with LLMs (Large Language Models) is highly desirable.
- A strong sense of ownership, team spirit, and commitment to excellence and integrity.
- Work with large, complex data sources to uncover insights, spot trends, and support decision-making.
- Design, build, and maintain statistical and machine learning models that solve business problems and enhance patient outcomes.
- Clean and transform raw data into usable formats through effective data wrangling techniques.
- Engage with stakeholders across technical and non-technical teams to identify challenges, define requirements, and propose data-driven solutions.
- Translate analytical findings into clear, actionable insights and present them in a compelling way to business leaders.
- Maintain, evaluate, and retrain models regularly to keep outputs relevant and accurate.
- Stay current with advancements in data science, cloud computing, and AI (including large language models).