Lead Data Scientist – Virtual Models

We are looking for a Lead Data Scientist – Virtual Models to take ownership of this programme from the start.

This is a hands-on role for an experienced data scientist who wants to build something new. You will assess existing datasets across QIMA ’s Business Units, identify the most promising opportunities, develop proof-of-concept models and prepare them for use by internal teams and, ultimately, by clients.

Initial applications may include models related to:

• skin;

• hair follicles;

• longevity;

• prostate cancer;

• immunology.

You will work closely with Managing Directors, laboratory scientists and QIMA ’s IT and AI experts. Scientific validation will be carried out in collaboration with independent laboratory teams.

Key responsibilities

• Review and assess proposals from QIMA ’s Business Units.

• Examine the underlying data in detail, including its quality, structure, consistency, depth and breadth.

• Recommend one or two models with the strongest potential for development, subject to approval by the CEO of QIMA Life Sciences.

• Build and manage the required data pipelines.

• Clean, structure, integrate and analyse complex biological datasets.

• Develop machine learning models and proof of concepts.

• Identify data gaps and recommend additional experiments where needed. Work with laboratory scientists to help define these experiments, including the required data, measurements and quality criteria.

• Establish clear validation criteria and prepare models for independent scientific validation.

• Improve the models through repeated testing and iteration.

• Ensure that code, data and models are reproducible, traceable and properly documented.

• Present results clearly to scientific, technical, commercial and non-specialist audiences.

• Support the preparation of a model for future commercialisation, either as a SaaS product or as a service supporting, replacing or complementing wet-lab assays.

• Contribute to the documentation and protection of intellectual property related to the models, pipelines and methods.

This role will initially be an individual contributor position. As the programme grows, you may help build and lead a team of additional data scientists.

First objectives

Within the first six months, you will be expected to:

• assess the main model proposals and their underlying datasets;

• recommend the one or two strongest projects;

• establish a proof of concept for one or two models.

Within 12 to 15 months, the objective will be to prepare at least one model for potential commercialisation. This should include sufficient scientific validation, an interface usable internally, and complete documentation or detailed operator training.

You have:

• a PhD in data science, biostatistics, bioinformatics, machine learning or a related field;

• 8–10 years of experience in data science, biostatistics and machine learning;

• a proven track record of delivering complex machine learning projects using complex biological datasets;

• strong experience building data pipelines, assessing data quality, identifying limitations and defining what additional data is needed;

• experience working with scientists and translating scientific questions into data and modelling approaches;

• excellent communication skills and the ability to explain complex concepts clearly to non-specialists;

• ideally, experience presenting work to clients, partners or other external stakeholders;

• a practical, entrepreneurial and hands-on approach;

• the ability to work independently and take ownership of an ambiguous, new project.

Experience in a specific therapeutic area is not essential. The most important requirements are strong technical expertise, experience with biological data and the ability to turn complex datasets into useful, validated models.

Strong Python or R skills, including production-quality, version-controlled code. Experience with cloud data platforms (we use Snowflake) and MLOps practices.

Why…