Senior/Principal Data Scientist - NLP (Remote) - United Kingdom

Jobleads
London
1 week ago
Create job alert

Senior/Principal Data Scientist - NLP (Remote) - United Kingdom, London

Veeva is a mission-driven organization dedicated to helping our customers in Life Sciences and Regulated industries bring their products to market faster. We value doing the right thing, customer success, employee success, and speed. Our teams develop transformative cloud software, services, consulting, and data solutions to enhance efficiency and effectiveness. Veeva supports a flexible work environment—work from home, at a customer site, or in an office.

Our product connects life sciences and key stakeholders to improve research and healthcare. It offers real-time academic, social, and medical data to build comprehensive profiles, aiding our industry partners in accelerating therapeutics development and clinical trials, ultimately helping patients receive urgent care sooner.

Role Overview

You will develop LLM-based agents specialized in searching and extracting detailed healthcare sector information about Key Opinion Leaders (KOLs). This includes creating an end-to-end human-in-the-loop pipeline to analyze unstructured medical documents (academic articles, clinical guidelines, meeting notes). These agents will perform semantic searches and provide precise answers across multiple languages and disciplines, utilizing cloud infrastructure for model development and deployment. Collaboration with software developers and DevOps engineers is essential.

Key Responsibilities

  1. Adopt the latest NLP technologies and trends in your platform.
  2. Develop LLM-based agents capable of function calls and tool utilization (e.g., browsers).
  3. Apply Reinforcement Learning from Human Feedback (RLHF) methods like DPO and PPO for training LLMs based on human preferences.
  4. Design and implement pipelines to extract information from large-scale, unstructured, multi-domain, multilingual data.
  5. Create semantic search functionalities to answer user queries effectively.
  6. Develop and utilize techniques such as named entity recognition, entity linking, slot-filling, few-shot learning, active learning, question answering, and dense passage retrieval.
  7. Analyze data models per source and region, and interpret model decisions.
  8. Collaborate with data quality teams to define metrics and evaluate models qualitatively and quantitatively.
  9. Utilize cloud infrastructure for development and work with teams to deploy models into production.

Minimum Requirements

  1. At least 4 years of experience as a data scientist (or 2+ years with a Ph.D.).
  2. Master's or Ph.D. in Computer Science, AI, Computational Linguistics, or related fields.
  3. Strong knowledge of NLP, Machine Learning, and Deep Learning.
  4. Experience with large language models and transformer architectures (e.g., GPT, BERT).
  5. Familiarity with large-scale data processing, preferably in the medical domain.
  6. Proficiency in Python and NLP libraries (NLTK, SpaCy, Hugging Face).
  7. Experience with BigData frameworks (Ray, Spark) and Deep Learning frameworks (PyTorch, JAX).
  8. Experience with cloud services (AWS, GCP, Azure) and containerization (Docker, Kubernetes).
  9. Strong collaboration and communication skills, adaptable to startup environments.
  10. Social competence, team-oriented, high energy, ambitious, and agile mindset.

Nice to Have

  • Background in Medical NLP.
  • Experience with training, fine-tuning, and serving LLMs.
  • Experience in the life/health science industry, particularly pharma.
  • Publications in AI peer-reviewed journals.
  • Production-grade development skills.
  • Leadership skills and a network for hiring and team growth.
  • Experience with NoSQL databases like MongoDB.
  • Familiarity with model registry solutions such as MLflow.
  • Experience with distributed computing platforms like Ray and Spark.

Perks & Benefits

  • Personal development budget.
  • Veeva charitable giving program.
  • Fitness reimbursement.
  • Life insurance and pension fund.

Veeva is committed to fostering an inclusive, diverse workforce. If you need assistance or accommodations during the application process, please contact us.

#J-18808-Ljbffr

Related Jobs

View all jobs

Senior/Principal Data Scientist - NLP (Remote) - United Kingdom

Senior/Principal Data Scientist - NLP (Remote) - United Kingdom (Basé à London)

Senior/Principal Data Scientist - Cross Indication (Basé à London)

Mid-Level/Principal Data Scientist

Principal Data Scientist - Marketing (Basé à London)

Senior Data Engineer

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Quantum-Enhanced AI in Data Science: Embracing the Next Frontier

Data science has undergone a staggering transformation in the past decade, evolving from a niche academic discipline into a linchpin of modern industry. Across every sector—finance, healthcare, retail, manufacturing—data scientists have become indispensable, leveraging statistical methods and machine learning to turn raw information into actionable insights. Yet as datasets grow ever larger and machine learning models become more computationally expensive, there are genuine questions about how far current methods can be pushed. Enter quantum computing, a nascent but promising technology grounded in the counterintuitive principles of quantum mechanics. Often dismissed just a few years ago as purely experimental, quantum computing is quickly gaining traction as prototypes evolve into cloud-accessible machines. When paired with artificial intelligence—particularly in the realm of data science—the results could be game-changing. From faster model training and complex optimisation to entirely new forms of data analysis, quantum-enhanced AI stands poised to disrupt established practices and create new opportunities. In this article, we will: Explore how data science has reached its current limits in certain areas, and why classical hardware might no longer suffice. Provide an accessible overview of quantum computing concepts and how they differ from classical systems. Examine the potential of quantum-enhanced AI to solve key data science challenges, from data wrangling to advanced machine learning. Highlight real-world applications, emerging job roles, and the skills you need to thrive in this new landscape. Offer actionable steps for data professionals eager to stay ahead of the curve in a rapidly evolving field. Whether you’re a practising data scientist, a student weighing up your future specialisations, or an executive curious about the next technological leap, read on. The quantum era may be closer than you think, and it promises to radically transform the very fabric of data science.

Data Science Jobs at Newly Funded UK Start-ups: Q3 2025 Investment Tracker

Data science has become an indispensable cornerstone of modern business, driving decisions across finance, healthcare, e-commerce, manufacturing, and beyond. As organisations scramble to capitalise on the insights their data can offer, data scientists and machine learning (ML) experts find themselves in ever-higher demand. In the UK, which has cultivated a robust ecosystem of tech innovation and academic excellence, data-driven start-ups continue to blossom—fuelled by venture capital, government grants, and a vibrant talent pool. In this Q3 2025 Investment Tracker, we delve into the newly funded UK start-ups making waves in data science. Beyond celebrating their funding milestones, we’ll explore the job opportunities these investments have created for aspiring and seasoned data scientists alike. Whether you’re interested in advanced analytics, NLP (Natural Language Processing), computer vision, or MLOps, these start-ups might just offer the career leap you’ve been waiting for.

Portfolio Projects That Get You Hired for Data Science Jobs (With Real GitHub Examples)

Data science is at the forefront of innovation, enabling organisations to turn vast amounts of data into actionable insights. Whether it’s building predictive models, performing exploratory analyses, or designing end-to-end machine learning solutions, data scientists are in high demand across every sector. But how can you stand out in a crowded job market? Alongside a solid CV, a well-curated data science portfolio often makes the difference between getting an interview and getting overlooked. In this comprehensive guide, we’ll explore: Why a data science portfolio is essential for job seekers. Selecting projects that align with your target data science roles. Real GitHub examples showcasing best practices. Actionable project ideas you can build right now. Best ways to present your projects and ensure recruiters can find them easily. By the end, you’ll be equipped to craft a compelling portfolio that proves your skills in a tangible way. And when you’re ready for your next career move, remember to upload your CV on DataScience-Jobs.co.uk so that your newly showcased work can be discovered by employers looking for exactly what you have to offer.