Senior Data Scientist

Job Summary

As the Lead AI Data Scientist, you will be a pivotal player in shaping our AI strategy for healthcare applications. You will spearhead our AI initiatives, driving innovation from clinical research to real-world deployment. This role requires a unique blend of deep technical mastery in AI, strategic thinking, and leadership. You will lead a high-performing team to build next-generation AI models that solve critical healthcare problems, improve patient care, and deliver significant clinical and business value.

Role & responsibilities

1. Healthcare AI Project Leadership:

   Lead the end-to-end lifecycle of healthcare AI projects, from problem formulation with clinical experts to model development for tasks like disease prediction, medical imaging analysis, and treatment personalization.

2. Team Mentorship & Management:

   Mentor, guide, and develop a team of data scientists. Foster a culture of collaboration, innovation, and clinical impact.

3. Advanced Clinical Model Development:

   Architect and implement state-of-the-art AI models using diverse healthcare data, including Electronic Health Records (EHR), medical imaging (e.g., MRI, CT scans), and genomics data. Special focus on Deep Learning for diagnostics, NLP for clinical text, and Generative AI for data synthesis or drug discovery.

4. Cross-Functional Collaboration:

   Work closely with clinicians, medical researchers, product managers, and regulatory teams to define the AI roadmap, identify high-impact clinical use cases, and ensure alignment with healthcare industry standards.

5. MLOps in Healthcare:

   Drive the MLOps strategy for the robust, scalable, and compliant deployment of AI models in clinical or operational settings.

6. Innovation & Research:

   Stay at the forefront of AI in medicine, exploring novel techniques and technologies to drive continuous innovation and maintain our competitive edge.

7. Clinical Impact Communication:

   Translate complex model outputs into clinically relevant and actionable insights for physicians, hospital administrators, and other healthcare stakeholders.

8. Quality & Governance:

   Establish best practices for data science in a regulated environment, ensuring model validation, fairness, interpretability, and compliance with data privacy standards (e.g., HIPAA principles, GDPR).

Preferred candidate profile

• Educational Background:

  Masters degree or Ph.D. in Computer Science, AI, Bioinformatics, Statistics, or a related quantitative field.

• Professional Experience:

  6+ years of hands-on experience in data science, with a strong preference for experience in the healthcare, pharmaceutical, or life sciences industry. At least 2 years in a leadership or senior mentorship capacity.

• Proven Track Record:

  Demonstrated experience in successfully leading and delivering complex AI/ML projects in a healthcare or clinical context.

• Technical Expertise:

• Deep expertise in machine learning, deep learning, NLP (including transformers and LLMs), and statistical modeling applied to healthcare data.
• Strong programming skills in Python and proficiency with AI/ML libraries (e.g., PyTorch, TensorFlow, Scikit-learn, Hugging Face).
• Solid understanding of MLOps principles and experience with relevant tools (e.g., Docker, Kubernetes, MLflow).

• Leadership:

  Excellent problem-solving abilities, a strategic mindset, and proven skills in leading and inspiring technical teams.

• Communication:

  Exceptional communication and presentation skills.