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

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