Job summary
This role is for an experienced Data Scientist with a strong foundation in machine learning and expertise in either agentic AI or optimization algorithms. The position involves leveraging data to solve complex business problems in healthcare technology, designing predictive models, and partnering across departments to drive impactful AI integrations. The candidate is expected to have extensive experience in Python production environments and strong mathematical and statistical skills.
Location and work setup
- Location
- Tromsø
- Work setup
- Remote
Salary
USD 194400.00–216000.00 year
Responsibilities
Use data science techniques to address meaningful business challenges with appropriate technical complexity. Collaborate with engineering, product management, and operations to define and execute data-driven strategies. Develop frameworks and metrics for monitoring data product performance and business impact. Research and propose solutions for operational and logistical issues. Lead design and evaluation of descriptive and predictive models. Promote the adoption of large language model (LLM) driven workflows across the organization and work with engineering to build production-ready autonomous agent-based systems.
Qualifications
At least 7 years of professional experience in complex problem solving and production-level Python development. Strong foundation in traditional predictive machine learning methods and the ability to evaluate when to use standard models versus large language models. Deep expertise in either optimization algorithms (linear/integer programming) or agentic AI system development and orchestration. Proven skill in setting success metrics and translating technical solutions into business outcomes. Excellent mathematics and statistics background, demonstrated by a degree in a quantitative field or equivalent experience. Effective communication skills to convey complex concepts to diverse audiences. Experience managing projects end-to-end with proactive issue resolution. Bonus experience includes causal inference, marketplace optimization, MLOps, cloud technologies, containerization, and handling sensitive health data under compliance standards.