Customer Data Scientist (Industrial Biotechnology)

Role Description

We’re looking for a Customer Data Scientist to ensure customer programs run smoothly and reliably . You will turn raw experimental data into clean, production-ready analytics that directly power our platform and customer deliverables. This role is crucial because it’s the quality gate and execution engine between the lab and production systems: if data is processed correctly and consistently, insights ship, reports stay dependable, and our software gets better with every dataset.

This role is focused on execution and reliability . You’ll work closely with the lab team and the customer-facing technical lead to run production analyses, maintain lab data pipelines, and generate reports/datasets using existing methods and tools.

How You’ll Work

• You’ll receive well-scoped work packages from a technical lead
• You’ll execute analyses in collaboration with our wet lab and dry lab teams and deliver results independently
• You’ll flag data quality issues, edge cases, and inconsistencies early
• Over time, you’ll help turn one-off analyses into standardized workflows

Key responsibilities

Customer & Lab Data Execution

• Process incoming laboratory data from customer and internal experiments
• Run production-ready analysis pipelines and algorithms on new datasets
• Generate structured outputs for downstream integration into our software platform
• Work with the lab team to ensure data completeness, correctness, and traceability

Data Pipelines & Quality Control

• Maintain and extend lab data processing pipelines
• Build and monitor QC checks, validation steps, and basic anomaly detection
• Create dashboards and summary views to track data health and experiment status
• Improve robustness and reproducibility of data flows over time

Reporting & Analysis

• Create customer-facing analytical reports (e.g. multi-strain comparisons, growth summaries)
• Standardize recurring analyses into reusable scripts or pipelines
• Support design-of-experiments (DOE) analyses for new lab iterations
• Clearly document assumptions, inputs, and outputs for each analysis
• Help move research or prototype scripts into stable, production-ready code
• Refactor and package commonly used analyses for reuse
• Contribute to internal documentation, runbooks, and templates
• Collaborate with software engineers to ensure clean handoff of results

Growth Outlook

As the company grows, this role can expand toward:

• Ownership of larger portions of the data pipeline
• Mentoring junior analysts or interns
• Leading standardization efforts across customer programs

Qualifications

• Bachelor’s or Master’s degree in data science, bioengineering, computational biology, statistics, or a related field
• 1–3 years of experience working with real-world scientific or laboratory data
• Strong Python skills (data processing, analysis, scripting)
• Experience working with structured datasets (tables, metadata, experimental results)
• Comfort running existing analyses and following established pipelines
• Clear written communication and documentation habits

Nice-to-have

• Experience with lab data, biological experiments, or industrial biotech. In particular, how to deal with the often messy or unstructured assets that already exist in organizations.
• Familiarity with QC, validation, or monitoring of data pipelines
• Exposure to DOE or experimental design
• Experience collaborating with software or platform teams
• Experience with computational biology or data-driven methods (classical machine learning, deep learning, or LLMs)
• Get on board early: Become an early part of a visionary team that is leveraging the power of digital biology to unlock the $4tn bioeconomy;
• Competitive compensation through salary + stock options; we are looking to build a small but mighty team and think about compensation along those lines;
• Leadership opportunity: Work closely with visionary founders and grow into a leadership role with significant responsibility;
• A beautiful office in the heart of Munich