This role puts you at the intersection of health, genetics, and real human performance. Youll join a team focused on developing advanced models and algorithms rooted in exercise physiology and genetic data, working on problems that are as scientifically complex as they are impactful. The environment is deeply technical, but purpose-driven, where machine learning isnt just about prediction accuracy, but about understanding the human body and translating that into actionable health insights. Responsibilities: Design and develop machine learning models and algorithms using physiological and genetic datasets Build and optimise data pipelines to process large-scale biological and performance data Translate complex datasets into meaningful health and performance insights Collaborate with cross-functional teams to integrate models into production systems Continuously improve model performance, data quality, and algorithm accuracy Requirements: 5 years experience in machine learning / data science / algorithm development Proven experience building ML models using real-world datasets Experience working with large, complex, structured and unstructured data Exposure to biological, physiological, or health-related data Apply now!

We are seeking a skilled Machine Learning Engineer to support the design, development, deployment, and operationalisation of machine learning and AI solutions within a banking environment. The role will focus on building scalable, secure, and production-ready ML solutions that support business decision-making, automation, risk management, customer insights, and digital innovation. What you'll do: Design, build, test, deploy, and maintain machine learning models and AI-driven solutions. Work with data scientists, data engineers, software engineers, architects, and business stakeholders. Translate business problems into practical ML/AI solutions. Develop ML pipelines for training, testing, deployment, monitoring, and retraining. Build APIs or services to expose ML models to business applications. Perform feature engineering, data preparation, experimentation, and model evaluation. Support MLOps practices including model versioning, monitoring, CI/CD, and automation. Monitor model performance, data quality, model drift, and production behaviour. Ensure solutions are scalable, secure, maintainable, and aligned to governance standards. Document model logic, technical designs, deployment processes, and support procedures. Your Expertise: 3 years’ experience as a Machine Learning Engineer, AI Engineer, Data Scientist, MLOps Engineer, or similar. Strong hands-on experience with Python (essential) and SQL. Experience with Scala, R, Java, or C++ would be advantageous. Experience developing and deploying ML models into production or enterprise environments. Strong understanding of machine learning algorithms, statistical modelling, feature engineering, and model evaluation. Experience with libraries/frameworks such as Scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM, Pandas, NumPy, or similar. Exposure to cloud platforms such as AWS, Azure, or GCP. Experience with MLOps concepts such as model deployment, monitoring, versioning, experiment tracking, retraining pipelines, and CI/CD. Experience with MLflow, Docker, Kubernetes, Airflow, Databricks, SageMaker, Azure ML, or Vertex AI would be advantageous. Banking, fintech, risk, fraud, payments, or customer analytics experience would be advantageous. Qualifications: Relevant qualification in Computer Science, Data Science, Statistics, Mathematics, Engineering, Information Systems, AI, or a related field. Relevant cloud, AI, ML, or data certifications would be advantageous. Technical Skills Python (essential) SQL Scala, R, Java, or C++ Machine Learning / AI Feature Engineering Model Deployment Model Monitoring MLOps Scikit-learn, TensorFlow, PyTorch Pandas, NumPy MLflow Docker / Kubernetes Git / CI/CD REST APIs Spark / PySpark / Databricks AWS / Azure / GCP Core Competencies Strong analytical and problem-solving ability. Strong coding and engineering mindset. Ability to move models from prototype to production. Good communication and stakeholder engagement skills. Comfortable working in cross-functional teams. Proactive, detail-oriented, and solution-focused. Nice-to-Have Generative AI / LLM experience. RAG, vector databases, embeddings. LangChain, OpenAI, Azure OpenAI, Amazon Bedrock, or similar. Model explainability and governance. Experience in regulated banking or financial services environments.