Data Scientist

About us

HealthBeacon specializes in innovative adherence solutions for medication management.

Our flagship product, the Injection Care Management System, improves medication adherence and delivers valuable data insights for patients, family members and clinicians alike. From supporting improved treatment outcomes to fostering a deeper understanding of patient needs, HealthBeacon

empowers patients and transforms the landscape of healthcare delivery.

In 2024, HealthBeacon was acquired by Hamilton Beach Health, a subsidiary of Hamilton Beach Brands Holding Co. (NYSE: HBB). This marked a significant step forward in our mission to enhance patient outcomes and strengthen our position in the healthcare industry.

About the role

A hybrid role based in Dublin this position plays a key role by collaborating with departments across the company to develop data-driven strategies

What will you do?

The Data Scientist II will be responsible for extracting, preparing and analysing complex data from multiple internal and external sources to generate advanced insights, predictive models and data-driven solutions that support business intelligence, product innovation and strategic decision-making. The role goes beyond descriptive analytics to include the development of machine learning models and scalable data products that unlock value from HealthBeacon’s historical and operational datasets. This position covers the full data science lifecycle, including data exploration, feature engineering, model development, validation and deployment. The Data Scientist will design and implement advanced analytical solutions, conduct statistical modelling and experimentation (e.g., A/B testing), and will contribute to automation initiatives that enhance efficiency and decision accuracy.

Working closely with cross-functional stakeholders, the Data Scientist will translate complex analytical outputs into clear, actionable insights, enabling evidence-based decision-making across the organisation while continuously identifying opportunities to leverage data for competitive advantage.

Qualifications & Skills

• 2–3 years of hands-on experience building and deploying end-to-end ML models/data products in a production or near-production environment.
• Added advantages: Hands-on experience processing large-scale audio or chat/text data (e.g., transcription pipelines, NLP, text classification, or conversational AI workflows).
• Solid understanding of core ML concepts: supervised/unsupervised learning, model evaluation, regularization, feature engineering, and model lifecycle management.
• Proficiency in Python and SQL for data manipulation, modelling, and pipeline development.
• Hands on experience with PowerBI & DAX
• Hands-on experience with AWS services such as Amazon S3, AWS Lambda, Amazon SageMaker — model training, tuning, hosting, and MLOps
• Strong verbal and written communication skills.
• Ability to identify trends and patterns with high attention to detail.
• Strong team player with personal initiative and ability to manage multiple priorities