Senior Data Scientist in United States at Jobgether

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Scientist based in the United States.

This is an impactful opportunity for a data science professional who enjoys solving complex, real-world problems in a fast-paced healthcare technology environment. In this role, you will develop and deploy machine learning models that directly improve operational efficiency, patient safety, and pharmaceutical intelligence outcomes. You will work across the full data science lifecycle, from problem definition and experimentation to production deployment and impact measurement. The position offers strong cross-functional exposure, collaborating with product, engineering, and customer stakeholders to translate ambiguous business challenges into measurable, data-driven solutions. You will also play a key role in strengthening data culture and advancing analytics capabilities across the organization. Ideal candidates are hands-on, customer-focused, and comfortable working at the intersection of applied machine learning and business strategy.

• 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.
• Contribute to improving team practices through code reviews, documentation, and knowledge sharing across the data organization.
• Support roadmap planning by assessing technical feasibility, risks, and opportunities for new data science initiatives.
• Promote best practices in experimentation, statistical rigor, and reproducible research across the team.

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.
• Familiarity with modern ML tooling and willingness to leverage AI-assisted development tools to accelerate experimentation.
• Experience in healthcare, pharmaceuticals, or regulated data environments is a strong plus.
• Exposure to MLOps practices, containerization, or CI/CD pipelines is considered advantageous.

Benefits

• Competitive salary with performance-based compensation opportunities.
• Fully remote work environment available across the United States.
• Comprehensive medical, dental, and vision insurance coverage.
• 401(k) retirement savings plan.
• Flexible and generous paid time off policy.
• Opportunity to work on high-impact healthcare AI and machine learning applications.
• Access to modern cloud infrastructure and advanced data science tooling.
• Collaborative, innovation-driven environment with strong technical mentorship.
• Professional growth opportunities in a fast-scaling healthcare technology organization.