ML Engineer​/Data Scientist

About the role

The Machine Learning Engineer / Data Scientist is responsible for designing, building, and optimizing machine learning models and systems that drive insights and automation across the organisation.

This role focuses on applying advanced algorithms to solve business problems, collaborating with data scientists, data engineers, and other teams to deploy scalable machine learning solutions. You'll ensure that models are production-ready, integrated into the data pipelines, and optimised for performance. This role plays a crucial part in transforming data into actionable insights and creating value through advanced analytics.

Model Development and Deployment

• Design, build, and train machine learning models using structured and unstructured data to support various business use cases
• Develop and optimise supervised, unsupervised, and reinforcement learning algorithms tailored to the organisation's needs
• Ensure seamless integration of machine learning models into production environments, collaborating with data engineers and software developers for deployment

Data Processing and Feature Engineering

• Work closely with data engineers to preprocess, clean, and organise data for model development
• Perform feature extraction and selection to enhance model accuracy and efficiency
• Develop robust ETL/ELT pipelines for managing data flows and preparing datasets for machine learning applications

Model Optimisation and Monitoring

• Optimise machine learning models for scalability and performance, ensuring they meet business and technical requirements
• Continuously monitor model performance, ensuring that predictions and outputs remain accurate and relevant
• Implement model retraining processes to ensure models adapt to changes in data over time

Research and Innovation

• Stay updated with the latest advancements in machine learning, AI, and related technologies, applying new techniques to improve existing models and processes
• Conduct research on emerging machine learning methods, identifying opportunities to implement cutting‑edge technologies

• Proficiency in machine learning frameworks such as Tensor Flow
  , Py Torch ,
  Scikit-learn
  , and Keras
• Experience in programming languages like Python and R, with strong skills in data manipulation and statistical analysis
• Deep knowledge of machine learning algorithms (regression, classification, clustering, deep learning) and their applications
• Knowledge and experience of Graph Database technologies, e.g.
  Neo4J
• Experience with cloud-based ML solutions, particularly on AWS (e.g., Sage Maker),
  Azure
  , Snowflake
  , or Google Cloud AI platforms
• Strong understanding of data structures, algorithms, and ETL processes
• Experience with SQL/No
  
  SQL databases and data management platforms
• Familiarity with model deployment using containerisation technologies like Docker and Kubernetes