At a Glance
- Tasks: Design AI solutions for drug discovery and optimise clinical trials in life sciences.
- Company: Join a leading tech firm transforming the pharmaceutical industry with data and AI.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with a chance to engage with C-level stakeholders.
- Why this job: Make a real impact in healthcare by leveraging cutting-edge AI technologies.
- Qualifications: 8+ years in data science with a focus on life sciences and strong programming skills.
The predicted salary is between 80100 - 97900 £ per year.
EPAM helps pharmaceutical and life sciences organizations use data, AI, and advanced analytics to transform R&D, clinical, and commercial functions. Its solutions address drug discovery, clinical development, pharmacovigilance, and patient engagement.
Responsibilities:
- Design and implement AI-driven solutions for drug discovery, clinical trial optimization, and pharmacovigilance;
- Advise executive stakeholders on AI strategy and translate technical concepts into actionable business outcomes;
- Define and deliver enterprise-wide AI and machine learning adoption roadmaps aligned with business objectives;
- Create governance frameworks to meet regulatory and ethical requirements, including GDPR, FDA, and EMA;
- Prototype Generative AI and LLM-based applications for literature mining, safety monitoring, and patient programs;
- Collaborate with data engineers, clinicians, and business teams to tailor solutions to client needs;
- Deploy AI and machine learning models in production using MLOps and LLMOps practices;
- Use cloud platforms and data solutions to ensure scalability and operational integrity;
- Present data-driven recommendations to senior audiences and demonstrate the impact of AI solutions;
- Support pre-sales activities, including solutioning, proposals, workshops, and industry presentations.
Requirements:
- 8+ years of data science experience focused on life sciences projects, ideally in consulting or enterprise settings;
- Practical experience delivering production-grade AI and machine learning solutions in regulated environments;
- Hands-on knowledge of Generative AI, LLM applications, and their relevance to life sciences use cases;
- Expertise in structured and unstructured life sciences data, including clinical and real-world datasets;
- Familiarity with NLP and deep learning techniques applied to biomedical data;
- Knowledge of Azure, AWS, or GCP and model deployment using MLOps frameworks;
- Strong Python and SQL programming skills, with experience in PyTorch, TensorFlow, and Hugging Face;
- Experience with Databricks, Spark, and other data platforms and big data frameworks;
- Excellent communication skills for engaging C-level stakeholders and aligning technical outcomes with business value;
- Nice to have: Experience applying AI in drug discovery, safety monitoring, or clinical development; understanding of biomedical ontologies or knowledge graph applications; knowledge of FDA and EMA regulatory frameworks and data privacy considerations; background in computational biology, biostatistics, or a related scientific discipline; familiarity with federated learning and privacy-preserving machine learning techniques.
nlp engineer in life sciences employer: Enfint
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We think you need these skills to ace nlp engineer in life sciences
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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✨Brush Up on Your Statistics
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