Research Scientist - Discovery Data integration

Research Scientist - Discovery Data integration

Evotec

Hamburg, Germany

Responsibilities

• Enable data‑driven early discovery by integrating and translating multimodal biological datasets into actionable insights that strengthen target validation and decision quality.
• Integrate, harmonize, and curate complex datasets across targeted assays, imaging, RNA‑seq/single‑cell, CRISPR screens, proteomics, and genomics.
• Partner with Early Discovery and Target Validation teams to shape study design, align analytical strategies, and interpret results in a biological context.
• Translate complex data into clear, decision‑relevant biological insights to support go/no‑go and portfolio prioritization decisions.
• Operationalize fit‑for‑purpose statistical and analytical approaches, working closely with bioinformatics and data science experts.
• Deliver high‑impact insight products (e.g. visual analytics, concise target insight reports) that enable transparent communication of findings.
• Establish and continuously improve data collection, metadata, and governance standards to ensure compliant, traceable, and reusable target validation data.
• Embed Design of Experiments (DoE) and Quality by Design (QbD) principles within laboratory and discovery workflows.
• Build scalable, automation‑ready data processes that maximize the value of perturbation and assay data across Evotec’s discovery portfolio.

Qualifications

• Ph.D. in Cell Biology, Molecular Biology, or a related field with proven experience in Computational Biology, Bioinformatics, or Systems Biology approaches supporting data‑driven decisions; or equivalent professional experience.
• 3–6+ years’ experience integrating, analyzing, and interpreting multimodal biological data in discovery settings (targeted assays, imaging/cell painting, transcriptomics incl. single‑cell, CRISPR screens, proteomics, genomics).
• Demonstrated expertise in statistics, DoE, and QbD for in‑vitro assays to ensure robust experimental design and decision‑making.
• Proficiency in R and strong data visualization skills.
• Proven collaboration and communication skills working across cross‑functional science and data teams.
• Familiarity with target validation and perturbation experimental approaches and laboratory processes.

Further ideal qualifications

• Experience with workflow/insight app automation (e.g., Shiny, Dash, Power BI) to scale analytics.
• Exposure to laboratory automation to streamline data capture and quality.