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Lead Data Scientist – Virtual Models

QIMA
London, GBR
Full-time
Lead · 8–10 years experience
Hybrid
Discovered 1 weeks ago

Full Job Posting

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:
  • 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 join QIMA?
  • This is an opportunity to build a new capability from the ground up and shape how QIMA uses its biological data. You will have direct access to senior leadership, laboratory experts and technical teams, with the autonomy to define the approach and deliver tangible results.
  • The role will be based in Europe and will require travel, depending on project needs, to QIMA laboratories in France, Northern Ireland and Germany.
  • Does this describe you? Then we want to hear from you as soon as possible! Apply now with your CV in English and we can write the next chapter of the QIMA story together!
  • At QIMA, we are on a mission to help our clients make products consumers can trust. We have developed compliance solutions for testing, inspection and certification (TIC) that enable supply chain agility, sustainability and product innovation. Our services are used by 30,000 businesses globally in the consumer products, agri-food and life sciences industries. Backed by 6,000+ dedicated Qimates worldwide, we bring local expertise and tailored support to every client. Our passion for service has made QIMA an industry leader for customer satisfaction, as highlighted by our exceptional Net Promoter Score (NPS).
  • Through our Life Sciences activities, we develop and deliver scientific testing and research services for our clients.
  • We are now launching a new programme to develop virtual pharmacology models. These models will use the data generated through years of laboratory work to help assess the efficacy of molecules and support future screening activities.

Qualifications

  • 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.

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