Data Scientist

About HealthBeacon

HealthBeacon develops medication adherence technology that helps people manage injectable treatments more effectively. Its core platform, the Injection Care Management System, is designed to strengthen adherence while giving patients, families, and clinicians practical data-driven visibility. By improving treatment outcomes and helping healthcare teams understand patient needs more clearly, the company aims to reshape how care is delivered.

In 2024, HealthBeacon became part of Hamilton Beach Health, a subsidiary of Hamilton Beach Brands Holding Co. (NYSE: HBB), marking an important expansion of its healthcare mission and market position.

Role overview

This Dublin-based hybrid position works across departments to shape data-led strategies and drive better decisions throughout the business.

What you will do

You will handle the end-to-end data science workflow, from sourcing and preparing data to building, validating, and rolling out models and analytical products. Your work will support business intelligence, product development, and strategic planning by turning complex information from internal and external sources into actionable insights.

The role extends beyond reporting and descriptive analysis. You will develop predictive models, perform statistical analysis and experimentation such as A/B testing, support automation efforts, and help build scalable data solutions that create value from historical and operational datasets.

Working with stakeholders across the company, you will convert technical findings into clear recommendations that can guide decisions and uncover new opportunities for competitive advantage.

Qualifications and skills

The ideal candidate brings 2 to 3 years of practical experience building and deploying machine learning models or data products in a production or near-production setting. A strong grasp of machine learning fundamentals is important, including supervised and unsupervised learning, evaluation methods, regularisation, feature engineering, and model lifecycle management.

You should be comfortable using Python and SQL for data preparation, modelling, and pipeline development. Experience with Power BI and DAX is required, along with exposure to AWS tools such as Amazon S3, AWS Lambda, and Amazon SageMaker for training, tuning, hosting, and MLOps workflows.

Additional exposure to large-scale audio or text-based data, such as transcription pipelines, NLP, text classification, or conversational AI, will be considered an advantage. Strong written and verbal communication, a detail-oriented approach, the ability to spot trends, and the capacity to manage several priorities while contributing positively to a team are all important.