1958 Data Scientist (Senior)

SUMMARY:
ESSENTIAL SKILLS:

• Strong communication skills with ability to engage both technical and non-technical stakeholders effectively
• Experience interpreting business needs and converting them into actionable proposals and implementations
• Proven background in Data Science or related technical field (statistics, computer science, engineering)
• Solid understanding of AI/ML model development lifecycle including training data curation, feature engineering,
• and model evaluation
• Familiarity with Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) approaches
• Experience collaborating with software engineers and IT teams to integrate and deploy AI systems
• Ability to translate technical concepts into business value
• Familiarity with MLOps practices and pipeline integration for productionising models
• Experience with cloud-based AI infrastructure and deployment considerations (scalability, reliability, security

• Hands-on experience with Python and data science tooling (model training, evaluation, hyperparameter tuning)
• Familiarity with containerisation and deployment tools (Docker, Kubernetes)
• Experience with cloud platforms and services for AI (AWS, Azure)
• Knowledge of MLOps/DevOps workflows, CI/CD (e.g., GitHub Actions)
• Familiarity with frontend concepts and APIs for integrating AI features into products
• Understanding of data governance, master data management, and data pipelines
• Experience with object detection/segmentation/classification use cases (where applicable)
• Knowledge of asynchronous architectures and AWS services (API Gateway, Lambda, S3, SQS)
• Comfortable coaching internal teams on low-code/no-code solutions to accelerate adoption
• Experience presenting technical roadmaps and ROI analysis to business stakeholders

POSITION INFO:
ROLE & RESPONSIBILITIES: Serve as the primary stakeholder lead for AI platform initiatives, managing expectations and aligning priorities across business and technical teams Translate business needs into AI solution proposals and drive them through to successful implementation Collaborate with data scientists and engineers to define solution blueprints, data pipelines, and model requirements Advocate for scalable, reliable, and secure deployment patterns for AI services and components Coordinate cross-functional workstreams to integrate AI models into production applications and processes Drive stakeholder workshops, training, and enablement to increase adoption and understanding of AI and ML capabilities Define success metrics, monitor model performance in production, and coordinate remediation where needed Manage prioritisation and roadmap discussions with product owners and engineering leads to ensure business value delivery Support the adoption of MLOps practices and CI\/CD pipelines to enable repeatable deployments Liaise with IT, security, and legal teams on governance, data privacy, and compliance requirements for AI systems. QUALIFICATIONS\/EXPERIENCE: Degree in Data Science, Computer Science, Statistics, Engineering, or equivalent relevant experience Minimum of 3-5 years' experience in data science, AI applications, or related fields with demonstrated stakeholder management experience Proven track record of designing or enabling AI\/ML\/Data Engineering solutions and working with crossfunctional delivery teams to deploy them into production

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