Data Scientist II

Position: Data Scientist II
Location: Remote (U.S. Based)
Type: Full-Time | C2C/W2

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).