3 of 3 Model Validation Jobs in Chelmsford

data science problems grounded in business scenarios Cover the full data science lifecycle, including: Data ingestion & cleaning Exploratory analysis Feature engineering Statistical analysis & modeling Validation and interpretation Implement verified Python solutions Ensure all problems are fully deterministic and reproducible Clearly document: Business context Data inputs & schemas Analytical logic Exact ...

frameworks, data pipelines, and cloud platforms, and is comfortable working in a fully remote, collaborative environment within the UK. Key Responsibilities 1. Machine Learning Model Development Design, develop, train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques … appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build and maintain APIs or batch pipelines for model inference. Monitor model performance, data drift, and retraining needs. 3. Data Engineering ...

Node.js backends. Collaborate with architects to define scalable and secure AI service architectures. Experience in the following Implementing AI/ML pipelines for model training, validation, and deployment (using tools such as MLflow, Vertex AI, or Azure ML). Manage model evaluation, drift monitoring, and continuous improvement … processes. Optimize inference performance and cost (e.g., model compression, quantization, API optimization). Ensure compliance with AI ethics, security, and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (Pinecone, Weaviate, FAISS ...