Data selection and quality evaluation for biological foundation models

Inceptive·Berlin, Germany or Palo Alto, CA or Zurichsenior$11,250–$20,000 USD/mo

En corto

→Analizar y mejorar calidad de datos biológicos para modelos de IA en desarrollo de fármacos.
→Diseñar experimentos y detectar sesgos/errores en datos usando estadística y programación.
→Trabajo híbrido en equipo global con enfoque antidisciplinario y foco en datos reales de biología.

Fluent communication in English required

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1. ¿Cómo identificarías artefactos de batch en un conjunto de datos de ensayos biológicos usando Python y estadística?

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¿Qué piden?

✓PhD o experiencia equivalente en biología computacional, bioestadística o disciplinas cuantitativas relacionadas
✓Historial comprobado en análisis de datos biológicos complejos con impacto en validación experimental
✓Capacidad para diseñar experimentos y evaluar variabilidad, sesgos y artefactos en datos biológicos
✓Programación sólida en Python y librerías científicas
✓Habilidades de comunicación claras entre equipos computacionales y experimentales
✓Disponibilidad para horarios cruzados (8am PT – 7pm CET) y viajar ocasionalmente

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

Pythonstatistical analysisexperimental designbiological datasetsmachine learning modelsdata visualizationassay qualityreproducibilitybatch effectsbiological function

¿A quién escribirle en Inceptive?

Tu dossier gratis identifica a las personas que te entrevistarían — con su background, qué valoran y cómo escribirles para destacar antes de aplicar.

At Inceptive, you will help pioneer the next generation of AI-designed drugs, with the potential to positively impact billions of people, as part of a collaborative, antedisciplinary team. We advance the state of the art in molecular design by training large-scale foundation models that enable cutting-edge generative approaches. Those models depend on rich, high-quality experimental data that captures biological function. Progress requires not only building better models, but also designing better experiments, understanding measurement systems, and generating datasets that faithfully represent underlying biology. You will collaborate closely with biologists and machine learning researchers to design, analyze, and improve the experiments that power our models. You will help determine what data should be generated, how experiments should be structured, how measurement artifacts can be identified, and how biological insights can be translated into scalable data generation strategies. Your Mission, should you choose to accept it • Embody our vision of an antedisciplinary environment and embrace learning about areas outside of your traditional area of expertise • Develop statistical and computational approaches to characterize assay quality, reproducibility, and sources of experimental variation • Identify and investigate sources of bias and measurement artifacts in biological datasets • Design and analyze large-scale biological experiments that generate training and evaluation data for machine learning models • Partner with experimental scientists to improve assay design, controls, and data collection strategies • Collaborate with machine learning researchers to understand how experimental design decisions impact model training and evaluation • Analyze, visualize, and communicate findings to support decision-making across scientific and engineering teams Qualifications • PhD in computational biology, systems biology, genomics, bioengineering, biostatistics, biophysics, or a related quantitative discipline, or equivalent practical experience • Demonstrated track record of analyzing complex biological datasets and translating computational insights into experimental validation or new data collection • Strong foundation in experimental design, statistical analysis, and quantitative reasoning • Deep understanding of sources of experimental variability, batch effects, and assay artifacts in biological data • Capable programmer in Python and common scientific computing libraries • Excellent written and verbal communication skills, including the ability to communicate effectively across computational and experimental disciplines • Availability to work with team members across US and Europe, with meetings starting at 8am PT and ending at 7pm CET • Readiness to travel several times a year for company retreats and business events • We value the benefits of in-person collaboration and expect candidates to primarily work from our office locations Preferred technical skills • 3+ years of post-PhD experience in computational biology, biostatistics, or a related field • Experience connecting experimental outcomes to machine learning model development and evaluation Compensation $135K – $240K + Bonus + Equity What we offer • A competitive compensation package • 30 days paid vacation per year • Comprehensive health insurance for US based Beginners • 401K with company match for US based Beginners and Direktversicherung for German Beginners • Quarterly company-wide retreats • Monthly wellness benefit • Budget for multiple visits per year to our of

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