We are seeking a hands-on AI Chapter Lead to drive the delivery of AI use cases and ensure technical excellence within our AI team. This role focuses on leading a team of data scientists, ensuring projects are delivered on time with high quality, and fostering the technical growth of team members. The ideal candidate combines strong technical depth with delivery focus—someone who can roll up their sleeves when needed while guiding the team through complex implementations.

Education & Experience:

Master's degree in Data Science, Statistics, Computer Science, Mathematics, or related quantitative field

4+ years of experience in data science/ML engineering, with minimum 1 years in a lead or senior technical role

Experience in banking/financial services is strongly preferred

Track record of delivering ML projects to production

Technical Skills:

Strong proficiency in Python and SQL

Solid knowledge of ML algorithms: regression, classification, clustering, ensemble methods, deep learning fundamentals

Hands-on experience with ML platforms (Dataiku preferred)

Understanding of MLOps practices: CI/CD for ML, model versioning, monitoring, containerization

Experience with cloud platforms (MS Azure preferred)

Familiarity with Git, Docker, and API development

Leadership & Soft Skills:

Demonstrated ability to lead small technical teams and deliver results

Strong mentoring and coaching capabilities

Clear communication skills—ability to explain technical concepts to non-technical stakeholders

Proactive problem-solver with strong ownership mentality

Collaborative approach with attention to quality and detail

PREFERRED QUALIFICATIONS:

Experience with LLMs, RAG architectures, or conversational AI implementations

Familiarity with model validation processes and regulatory documentation requirements

Experience with Agile/Scrum methodologies in data science context

Background in NLP or computer vision projects

• Opportunities for professional growth and development
• Competitive salary and bonuses
• Comprehensive insurance coverage
• Supportive work environment
• Visa Premium salary card
• Corporate discounts and events
• Additional vacation days
• Discounted education and employee loans
• New cozy studio office in Port Baku Tower 2
• Strong teamwork with Agile principles, regular team buildings and themed events
• Multicultural environment with foreign colleagues sharing their best experiences

AI Use Case Delivery:

Own end-to-end delivery of assigned AI use cases, ensuring projects progress from development through deployment with clear timelines

Manage sprint planning, task allocation, and daily stand-ups to maintain delivery momentum

Remove technical blockers, coordinate with dependent teams (IT, Data Engineering), and escalate issues proactively

Ensure quality standards through code reviews, testing practices, and documentation requirements

Technical Leadership:

Provide hands-on technical guidance on model development, feature engineering, and deployment approaches

Establish and enforce coding standards, version control practices, and MLOps workflows

Lead technical design discussions and architecture decisions for use cases within scope

Stay current with ML/AI advancements and introduce relevant techniques to the team

Team Development:

Mentor and coach data scientists, providing regular feedback and career guidance

Conduct technical skill assessments and create individual development plans

Organize knowledge sharing sessions, tech talks, and learning initiatives within the chapter

Support hiring processes including technical interviews and candidate evaluation

Model Operations & Monitoring:

Ensure deployed models meet performance standards through monitoring and alerting frameworks

Coordinate model retraining cycles and performance optimization efforts

Maintain documentation standards for model handover and operational support

Collaborate with Model Validation team on documentation requirements and review processes

Stakeholder Coordination:

Serve as primary point of contact for business stakeholders on delivery status and technical feasibility

Translate business requirements into technical specifications for the team

• Coordinate with platform team on infrastructure and tooling requirements

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