Data Scientist

We are seeking a highly skilled Python Expert with a strong background in Natural Language Processing (NLP and LLMs. The ideal candidate will have experience in applying state-of-the-art (SOTA) models to new problem statements, particularly in the clinical field. We are looking for individuals who can not only understand and dissect existing SOTA models but also possess the ability to explain their applicability to our unique challenges.

Responsibilities:

• Develop and maintain Python-based NLP algorithms and models for various clinical applications.
• Analyse existing SOTA models and adapt them to address specific clinical problem statements.
• Collaborate with cross-functional teams to define project requirements and objectives.
• Clearly explain the applicability and limitations of SOTA models for new problem statements to both technical and non-technical stakeholders.
• Deploy, monitor and maintain production-grade models to ensure their performance and stability.
• Continuously research and stay up-to-date with advancements in NLP, Signal Processing, and Biomedical domains.

Requirements:

• Bachelor's degree in Computer Science, Data Science, or a related field.
• 3+ years of experience in Python programming and expertise in NLP or Signal Processing.
• Familiarity with SOTA models and their applicability in the clinical field.
• Experience in deploying and monitoring production-grade models.
• Strong problem-solving and analytical skills.
• Excellent communication and presentation abilities, with the capacity to explain complex models to diverse audiences.
• Ability to work effectively in a team-oriented environment and manage multiple projects simultaneously.

Preferred qualifications:

• Prior experience in a healthcare background is preferred (but not mandatory).
• Experience in the Biomedical domain, especially working with biomedical/clinical data and challenges.
• Experience in creating monitoring and observability systems around LLMs is a plus.
• Familiarity with popular NLP and Signal Processing libraries and frameworks such as TensorFlow or PyTorch.
• Knowledge of cloud platforms and containerization technologies like Docker or Kubernetes.