Senior Solution Data Scientist in Hofheim am Taunus, Deutschland

ABOUT THE COMPANY

Our company develops advanced software for biopharmaceutical process development and manufacturing. Our platform combines AI, mechanistic modeling, and bioprocess expertise to build predictive digital twins that support process understanding, optimization, and better decision-making. We work with leading biopharmaceutical companies and are building a high-quality SaaS platform with a strong focus on usability, reliability, maintainability, and scientific value.

POSITION OVERVIEW

We are seeking a Solution Data Scientist Lead to join our Customer Solutions team. In this role, you will lead customer projects end-to-end: from data intake and preprocessing to building hybrid models using our proprietary technology and delivering scientifically sound, actionable insights. Just as importantly, you will drive customer adoption of our platform: your mission is to move each customer along the arc from "we model for you" to "you model in the platform, we advise" — building customers who are successful, self-sufficient users of the product.

You will act as a key technical interface between our customers, our modeling workflows, and our platform capabilities. This role requires someone who can understand biological processes, interpret model outputs critically, and communicate results clearly and effectively to both scientific and non-technical stakeholders.

In addition, you will help translate customer needs and emerging use cases into internal modeling and product requirements. You will work closely with our process modeling and product teams to identify opportunities for new technical capabilities, workflow improvements, and future model development based on customer demand and real-world application needs.

This position is ideal for a data scientist with strong analytical depth, scientific curiosity, customer-facing confidence, and a strong instinct for identifying how customer challenges can inform the next generation of modeling capabilities.

KEY RESPONSIBILITIES

Customer Project Execution

• Lead and manage customer modeling projects from data intake through model delivery and result communication, using our platform as the primary delivery vehicle wherever possible.

• Build, calibrate, and validate hybrid models combining mechanistic and AI-based approaches.

• Translate biological and process data into predictive models of cell culture and bioprocess behavior.

• Ensure scientific rigor, reproducibility, and clear documentation throughout project execution.

Product Adoption and Customer Enablement

• Onboard customers onto our platform and own their time-to-first-value: from data intake to the first in silico experiment that informs a real process decision.

• Train and coach customer scientists to build, validate, and use their own models in the platform — growing customer self-sufficiency quarter over quarter.

• Convert every manually executed workflow into a documented product requirement; what is done by hand today defines the product backlog of tomorrow.

• Champion the customer's experience of the platform: report usability gaps and adoption blockers directly into product prioritization.

Data Science and Modeling

• Apply statistical modeling, machine learning, and hybrid modeling approaches to bioprocess data.

• Design and execute virtual experiments to support process understanding, prediction, and optimization.

• Evaluate model quality, interpret results critically, and communicate limitations appropriately.

• Prepare high-quality analyses, visualizations, and presentations tailored to different audiences.

Customer Engagement and Scientific Communication

• Communicate results and insights effectively to both scientific and non-technical stakeholders.

• Work closely with customers to define modeling goals, interpret outcomes, and identify opportunities for additional value creation.

• Serve as a trusted technical contact during and after project execution.

Pre-Sales and Commercial Support

• Lead technical evaluations and pilot projects with prospective customers, demonstrating model performance on their own historical data.

• Support commercial colleagues with scientific scoping of proposals, and contribute delivered-value evidence to expansion and renewal conversations.

• Develop reference stories and business-value reviews with existing customers that support marketing, sales, and the launch of the platform.

Capability Development and Opportunity Identification

• Identify recurring customer needs, unmet technical requirements, and emerging scientific use cases.

• Translate customer feedback and project learnings into clear input for the process modeling and product teams.

• Help uncover new technical opportunities to expand and strengthen our modeling capabilities.

• Contribute to shaping future modeling workflows and solution offerings based on real customer challenges and market needs.

Cross-Functional Collaboration

• Collaborate closely with application scientists, process modelers, ML engineers, and product teams to improve modeling workflows and customer delivery.

• Contribute to the continuous improvement of internal tools, best practices, and scientific workflows.

• Provide structured feedback from customer use cases to help guide future modeling and product development.

SUCCESS METRICS

• Time-to-first-value: weeks from customer data intake to the first model-informed decision the customer acts on.

• Customer self-sufficiency: the share of in silico experiments run by customer scientists themselves in the platform, growing quarter over quarter.

• Reference-ability: customers willing to act as references or co-present their results.

• Account expansion: additional molecules, sites, or teams adopting the platform within existing accounts.

TECHNICAL SKILLS

• Strong background in data science, applied mathematics, or computational biology.

• Proficiency in Python and relevant data science/ML libraries (NumPy, Pandas, SciPy, PyTorch, JAX, Scikit-learn).

• Understanding of mechanistic modeling concepts (ODEs, kinetic models, or hybrid modeling approaches).

• Familiarity with bioprocess or omics data is highly desirable.

• Profound experience in data visualization, model validation, and result presentation.

• Working knowledge of cloud environments (Azure, AWS, or GCP) is a plus.

SOFT SKILLS

• Outstanding communication skills, with the ability to explain complex modeling concepts clearly to both scientific and non-technical audiences.

• Strong ownership and the ability to manage customer-facing projects independently.

• Ability to connect technical findings to customer objectives and practical business value.

• Strong scientific curiosity and a proactive mindset for identifying new modeling opportunities.

• Ability to translate customer needs into structured internal recommendations and requirements for technical and modeling development.

• High level of analytical thinking and structured problem-solving.

• Confidence working across cross-functional teams in a remote environment.

• Motivation to make yourself progressively less necessary to each customer — success is the customer achieving results independently in the product.

PREFERRED QUALIFICATIONS

• MSc or PhD in Data Science, Systems Biology, Bioprocess Engineering, Computational Biology, or related field.

• Prior experience in a customer-facing data science or scientific consulting role.

• Experience with hybrid or mechanistic modeling of biological systems.

• Familiarity with biopharmaceutical development or process modeling workflows.

WHY JOIN US?

At our company, you will work at the forefront of hybrid modeling, AI, and digital twins for biopharmaceutical process development. You will collaborate with an international team of experts in modeling, machine learning, and systems biology while working directly with leading biopharma customers on scientifically meaningful problems. In this high-impact role, you will not only deliver value to customers but also help shape the future direction of our modeling capabilities by identifying new technical opportunities based on real-world needs.