Data Scientist in the Explorative Biochemical Analytics Department, R&D Nov at Novonesis
Hørsholm, , Denmark -
Full Time


Start Date

Immediate

Expiry Date

24 Sep, 25

Salary

0.0

Posted On

18 Aug, 25

Experience

3 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Learning Techniques, Ethnicity, Dash, Statistics, Denmark

Industry

Information Technology/IT

Description

JOIN US AS OUR NEW DATA SCIENTIST IN THE EXPLORATIVE BIOCHEMICAL ANALYTICS DEPARTMENT, R&D NOVONESIS

Are you passionate about developing data flows, and solving real-world problems in biotechnology using data-driven decisions? Do you have experience with databases and a mindset for creating solutions for users to work smarter and not harder? Then you might be our new data scientist.
We’re looking for a Data Scientist who thrives in a collaborative environment and is excited to work across a broad variety of data types and scientific workflows. You’ll play a key role advancing our digital infrastructure by designing data pipelines and creating intuitive visualizations to support our scientists and business stakeholders. Furthermore, you will be anchored in key R&D projects to contribute with advanced data analysis for the development of new biosolutions.

OTHER SKILLS THAT ARE PREFERABLE:

  • Hands-on experience building apps using Dash, Streamlit, or similar platforms.
  • Strong foundation in statistics and multivariate data analysis techniques.
  • Practical experience in applying machine learning techniques (e.g., regression, classification, clustering) to analyze and interpret complex biological or biochemical datasets.
  • Experience working with large-scale scientific datasets, such as omics data, or time-series laboratory data.
    Location : This job will be located at Novonesis Innovation Campus in Hørsholm, Denmark.
    Application deadline : August 25th 2025. Please apply as soon as possible as we will continuously screen the applicants.
    Contact details : Department Manager Bruna Marques dos Santos, e-mail: bmds@novonesis.com
    Could our purpose be yours? Then apply today!
    At Novonesis we commit to an inclusive recruitment process and equality of opportunity for all our job applicants. We recommend you not to attach a cover letter to your application. Instead, please include a few sentences in your resume/CV about why you are applying. To ensure a fair recruitment process, please refrain from adding a photo in your resume/CV.
    Novonesis is dedicated to fostering a unique community by embracing and respecting differences. We make all employment decisions based on business needs, ensuring that every individual can thrive, regardless of identity or background such as ethnicity, religion, gender, sexual orientation, age, disability, or veteran status.

How To Apply:

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Responsibilities

OUR PURPOSE POINTS THE WAY

In Novonesis, we know that solutions rooted in biology can help solve humanity’s biggest challenges. Since we began more than a century ago, this has been our guide. It’s how we’ve gotten so far. And it’s how we’ll impact the future. Now, more than ever, the world needs change. And with biosolutions, the possibilities for transformation are endless.

KEY RESPONSIBILITIES:

  • Design and implement data pipelines for diverse biochemical analyses, ensuring seamless integration between our laboratories and data infrastructure.
  • Collaborate closely with scientists and lab technicians to understand experimental workflows, ensure accurate data capture, and provide tailored data solutions to support research objectives.
  • Build data solutions and interactive visualizations to provide researchers with “one-click” access to analyses, dashboards, and metrics.
  • Collaborate with scientists to solve data challenges (e.g., developing apps, datasets, performing biochemical data analysis, etc).
  • Represent the team in data governance initiatives across Novonesis
  • Provide mentorship and training to team members on data science methodologies, tools, and best practices.
  • Define and implement best practices for managing FAIR biochemical data.
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