Data Scientist

Quick Facts

• Role focuses on designing, building, and operationalizing AI and machine learning solutions.
• Coverage spans Data Science, AI Engineering, and MLOps.

Description

You will design, develop, and productionize innovative AI and machine learning solutions for real business needs. The role includes building AI applications and services, implementing modern ML/GenAI approaches (including LLMs and RAG), and maintaining robust MLOps pipelines from experimentation through deployment and monitoring. You’ll collaborate across engineering, data, architecture, and business stakeholders to move prototypes into reliable production systems.

Responsibilities

• Design, develop, and productionize AI/ML solutions addressing business challenges
• Explore and implement Machine Learning, Generative AI, LLMs, and intelligent automation approaches
• Build AI applications and services using Python, APIs, microservices, and cloud-native technologies
• Create reusable AI/ML libraries, frameworks, and common components
• Design and maintain ML/AI pipelines across the full lifecycle (training, deployment, monitoring, improvement)
• Build scalable, secure, and maintainable production systems for AI workloads
• Apply MLOps/DevOps/software engineering practices to AI solutions
• Work with Docker, Kubernetes, cloud platforms, CI/CD, model registries, monitoring, and automation tooling
• Collaborate with technical and business stakeholders to deliver reliable solutions
• Contribute to engineering standards, architecture principles, and reusable patterns
• Stay current on emerging AI technologies and evaluate value for clients

Requirements

• 4+ years of professional experience in software engineering, data science, machine learning, MLOps, AI engineering, or closely related fields
• University degree in Computer Science, Mathematics, Physics, Engineering, or equivalent practical experience
• Strong problem-solving skills and an engineering mindset
• Understanding of good software engineering and application design practices
• Ability to work in an Agile, collaborative environment
• Curiosity and continuous learning mindset as AI evolves
• Ability to communicate technical ideas clearly and collaborate with technical and non-technical stakeholders
• Experience in banking/financial services or other regulated industries is particularly welcome

Benefits

• Opportunity to experiment, build, scale, and influence how AI is applied in practice
• Work with a collaborative, entrepreneurial team across engineering, data, architecture, and financial services
• Chance to design, build, and operationalize next-generation intelligent solutions