nlp engineer in life sciences

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.

• 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.

• 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.

• No conditions specified