Data Scientist

Hilobyaktiia; Germany (OH); Hybrid; USD 90,000 - 140,000; Full time

Job summary

Hilo by Aktiia is seeking a Data Scientist to turn large real-world and clinical datasets into robust analyses, models, and actionable insights. You will work across clinical, scientific, product, and engineering teams with high autonomy from data preparation to interpretation.

The role emphasizes publication-ready outputs, rigorous statistical methods, and collaboration on experiments, while contributing to our Python/SQL/Spark codebase in a hybrid/remote-first environment.

Qualifications

• MSc or equivalent in Data Science, Statistics, Biostatistics, or quantitative field.
• 3–5 years of hands-on data science experience preferred.
• Strong Python and SQL proficiency; Spark is a plus.
• Solid foundations in statistics and experimental design.
• Ability to translate complex questions into defined analytical problems.

Responsibilities

• Mine and analyze large-scale real-world data to identify patterns and outcomes.
• Design and interpret statistical analyses of clinical data and endpoints.
• Produce publication-ready figures and statistical summaries for reports.
• Apply methods to user and product data to answer engagement and usage questions.
• Design, run, and interpret experiments to quantify impact of changes.
• Develop reproducible analytical workflows with well-documented code.

Skills

Python

SQL

Spark

Statistical methods

Education

MSc in Data Science

Tools

Git

CI/CD

Job description

About Hilo by Aktiia

High blood pressure is the world's most common disease, causing 18 million deaths each year. At Hilo by Aktiia, our vision is a world where no lives are lost or damaged from the effects of high blood pressure. Our mission is to make this a reality by developing tech to help people control their blood pressure. We are a venture-backed scale-up, having raised over $96M from investors in Europe and the United States.

Our technology - rooted in 18 years of research at the Swiss Center for Electronics and Microtechnology (CSEM) - is the world's only medically accurate, continuous blood pressure monitor that is cuffless in daily life in the consumer space. Validated through extensive clinical trials and CE Marked as a Class IIa medical device, our solution is now available in 12 countries.

In July, we launched in the US - a major step in bringing Hilo's technology to the millions of Americans living with high blood pressure. We are a hybrid/remote-first company, headquartered in Neuchâtel, Switzerland, united by our passion for impact and innovation.

Role Overview

We're looking for a Data Scientist who can turn large, complex real-world and clinical datasets into robust statistical analyses, models, and actionable insights. You will work on clinical, scientific, product, and business questions and translate data into findings that support scientific publications and informed decision-making across the company. This is a hands-on role with a high degree of autonomy, where you will independently take analytical questions from data preparation through statistical analysis and interpretation, while collaborating closely with clinical, scientific, product, and engineering teams.

Requirements

• Mine and analyse large-scale real-world data from Aktiia's databases to identify patterns, trends, and relationships related to clinical outcomes, product performance, accuracy, and usage
• Design, perform, and interpret statistical analyses of clinical study data, working with the clinical team on study endpoints, protocols, and appropriate analytical approaches
• Produce analytical results into clear scientific insights, publication-ready figures, and statistical summaries that support conference presentations, posters, and peer-reviewed publications
• Apply statistical and analytical methods to user and product data to answer questions around engagement, retention, conversion, feature usage, and segmentation
• Design, run, and interpret experiments and statistical tests to evaluate product or feature changes and quantify their impact
• Develop reproducible and traceable analytical workflows, with well-documented code, analyses, and results in line with quality and regulatory standards (ISO 13485, MDR, GCP).
• Contribute to our shared Python/SQL/Spark codebase through Git, CI/CD, and code review practices
• Select and apply appropriate statistical and modelling techniques to answer clinical, scientific, product, and business questions, assessing assumptions, limitations, and the reliability of results

What Success Looks Like

You can independently take analytical questions from data preparation through analysis and interpretation, producing reliable and reproducible results. Your analyses generate clear insights that contribute to scientific publications and help Product and Leadership make informed decisions. You collaborate effectively across clinical, scientific, product, and engineering teams and communicate analytical findings clearly to both technical and non-technical audiences.

What we're looking for

• MSc (or equivalent demonstrated ability) in Data Science, Statistics, Bioinformatics, or another quantitative field
• 3-5 years of hands-on experience in Data Science, Statistics, Biostatistics, Data Analytics, or a related quantitative field. We care more about proven ability than an exact number of years, so strong candidates with less experience are very welcome to apply
• Strong proficiency in Python and SQL. Experience with Spark is a strong advantage.
• Strong statistical foundations, including hypothesis testing, regression, survival analysis, experimental design, and statistical modelling.
• Ability to select appropriate methods, assess assumptions and limitations, and interpret results clearly.
• An understanding of clinical study design and biostatistics is important
• Strong analytical problem-solving skills - you can translate ambiguous clinical, scientific, product, or business questions into well-defined analytical problems and identify appropriate approaches to answer them.
• Comfortable turning large, messy, real-world datasets int