Lead Data Scientist

About the Role

We are seeking an experienced and visionary Lead Data Scientist to lead our advanced

analytics and AI initiatives. This role demands deep technical expertise, strategic thinking, and

leadership skills to drive innovation and ensure the development and deployment of high-quality,

compliant AI/ML solutions. The ideal candidate will excel at navigating complex regulatory

environments, designing scalable systems, and mentoring cross-functional teams.

Key Responsibilities

1. Strategic Leadership:

• Drive the organization’s AI/ML strategy, ensuring alignment with business goals and compliance with regulatory frameworks.
• Mentor and lead a team of data scientists and engineers to foster innovation and technical excellence.

2. Regulatory Compliance:

• Leverage expertise in Model Risk Regulatory Knowledge to ensure that AI/ML models meet industry standards and compliance requirements.
• Implement and manage frameworks for ethical AI, focusing on bias detection and fairness in model outcomes.

3. Model Development & Deployment:

• Design, develop, and deploy state-of-the-art ML models and architectures, utilizing tools like SageMaker, Bedrock, Code Whisperer, and Azure ARM.
• Oversee end-to-end Model Lifecycle Management using platforms such as MLflow.

4. Infrastructure and Tools:

• Build and maintain scalable systems for AI/ML operations using CI/CD tools and containerization technologies (e.g., Docker, Kubernetes).
• Integrate cutting-edge generative AI services into enterprise workflows, ensuring scalability and robustness.

5. Data Analysis and Insights:

• Apply advanced data analysis tools and programming languages such as Python and SQL to extract actionable insights from complex datasets.
• Monitor and optimize model performance, identifying areas for continuous improvement.

Required Skills and Qualifications

• Master's or Ph.D. in Computer Science, Data Science, Mathematics, Statistics, or a related field.
• Technical Expertise:

Proficiency in ML tools and frameworks (e.g., TensorFlow, PyTorch).

Hands-on experience with cloud-based AI services (AWS, Azure, etc.).

Strong command of Python, SQL, and data analysis libraries.

Expertise in model lifecycle management tools (e.g., MLflow) and CI/CD pipelines.

• Industry Knowledge: In-depth understanding of Model Risk Regulatory frameworks and guidelines. Familiarity with bias detection tools and ethical AI practices.
• Soft Skills: Excellent problem-solving and critical-thinking abilities. Strong leadership and communication skills to collaborate with technical and non-technical stakeholders.
• Certifications in cloud platforms like AWS, Azure, or Google Cloud.
• Experience implementing generative AI models and tools for business applications.
• Knowledge of container orchestration tools like Kubernetes.