9 beste Stellen als full stack software developer in berlin pankow – aktuell auf Personalsuche

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Data Scientist, Optimizations

Abacus Medicine -
Berlin

Jetzt bewerben

Details zur Stelle

Vollzeit

Qualifikationen

Azure
NumPy
Git
Künstliche Intelligenz
Kommunikationsfähigkeit
Python
Analytik

Vollständige Stellenbeschreibung

Drive meaningful change

Join us as a Data Scientist, Optimizations and contribute to the development of quantitative solutions and decision support models across Abacus Medicine Group, helping drive the development of our next generation of decision support algorithms.

Get a glimpse of our world

You will be joining the Advanced Analytics team within Abacus’s Data, Analytics & AI (DnA) department. Our data scientists and data analysts collaborate closely with line-of-business product owners & senior management to develop and operate AI/ML algorithms & models that support decision making in Abacus’s core business processes. Additionally, we have a close partnership with Abacus’s BI developers, data engineers, GenAI specialists, and software teams, working together to deliver impactful solutions via our Azure Databricks platform. We take pride in our multi-cultural, cross-disciplinary roster of data & AI specialists, and would look forward to welcoming your unique perspective to our team.

Are you ready to embark on this journey with us?

In this role, you will work with quantitative solutions across the advanced analytics product lifecycle. This includes contributing to use case discovery with the business, developing and testing mathematical models, and supporting the implementation of solutions together with engineering teams.

Your main responsibilities include:

Developing, maintaining and improving optimization models to support decision making across Abacus Medicine Group. This role has a specific focus on quantitative models (i.e. optimizations, operations research, simulations, forecasting)
Working closely with business stakeholders to understand and translate challenges into impactful, data-driven solutions, particularly within procurement, supply chain, and portfolio management experts
Exploring, prototyping and recommending appropriate solutions for business challenges. This role is expected to lean in to learning, iterating, and failing fast to deliver, and can recognize when simple solutions are preferable to complex algorithms
Collaborating with data scientists & engineers, software developers, and product owners to deploy end-to-end decision support solutions anchored on our Azure Databricks Advanced Analytics platform
Contributing to the development of expertise in optimization & OR methodologies within the team and sharing knowledge across teams and project groups.
Contributing cross-functionally to internal DnA including directly participation in Advanced Analytics team initiatives and deliveries (i.e. platform upgrades and agentic coding adoption)

Fuel for your success in the role

We imagine you have a strong foundation in data science and applied mathematical modelling and are motivated by using quantitative methods to solve real business problems. You are comfortable working with Python-based tools and have experience or interest in developing optimization models and decision support solutions. Whether your experience comes from industry roles, internships, or academic projects, you are able to approach complex questions in a structured and analytical way.

Experience within areas such as inventory optimization, dynamic pricing, or demand forecasting is beneficial but not required. What matters most is your curiosity and ability to translate business challenges into workable analytical solutions. You enjoy collaborating with others and communicating your findings clearly across both technical and business stakeholders.

Specifically, your qualifications should include:

Experience developing, maintaining or improving optimization algorithms and/or AI/ML models, ideally as part of a full-stack data and analytics team
Exposure to linear programming, operations research, and mathematical optimization methodologies, either in a professional or an academic context
Fluency in Python frameworks for data science such as pandas, numpy & Scikit-learn, as well as expertise in Python-based optimization libraries such as (i.e. cvxpy, PuLP)
A graduate degree based on a quantitative foundation – disciplines such as mathematics, statistics, data science, computer science and engineering are all relevant; the title of the degree is less important than the analytical experience underlying it
Well-rounded communication skills and the ability to discuss both complex technical topics and commercial business needs
This role requires fluent English.

It’s of advantage if you have experience with:

Formulating and solving optimization problems
Exposure to optimization methodologies incorporating uncertainty
Developing & delivering analytical solutions within an agile way-of-working or team contex
Developing on a modern data science stack including version control (Git), cloud data platforms (Azure/Databricks), and AI developer tools (Claude Code/Cursor)

Are you interested?

If you want to apply for the position, please follow the link and attach your CV and cover letter.

Would you like to know more about us?

Moving Healthcare. Moving You.

In Abacus Medicine Group, you’ll experience the power of Moving Healthcare. With our change mindset, we're moving the industry and improving healthcare as we broaden access to medicine.

But it's not just about moving healthcare, it's about Moving You. You’ll be part of a caring community of more than 1,750 people across the World. With us, you will be valued, empowered, and have a direct impact on driving change. Let's access the future of medicine and make a difference together. Your move.

Department

Digital

Locations

Berlin

Employment type

Full-time

Jetzt bewerben

Erweiterte Suche

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Drive meaningful change

Get a glimpse of our world

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Fuel for your success in the role

Are you interested?

Would you like to know more about us?

Moving Healthcare. Moving You.

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