Role / Job Title: Data Solution Designer Data Science Work Location: Norwich 3 Days (Flexible) Duration of Assignment: 06 Months The Role The Data Solution Designer Data Science is responsible for designing end to end data science and advanced analytics solutions that translate complex business problems into scalable, secure, and high performance data products.This role bridges business stakeholders, data engineering, data science, and IT architecture teams, ensuring solutions are production ready and aligned with enterprise standards. Your Responsibilities Solution & Data Model Design 1. Solution Design & Architecture Design end to end data science solutions including data ingestion, feature engineering, model development, deployment, and monitoring Define logical and physical architectures for analytics platforms, ML pipelines, and AI products Ensure solutions are scalable, reusable, secure, and cost effective Select appropriate ML/AI techniques (e.g., regression, classification, NLP, forecasting, clustering) 2. Data & Analytics Engineering Alignment Work closely with data engineers to define: Data models and schemas Data quality rules ETL / ELT pipelines

Define feature stores, training datasets, and inference pipelines 3. Model Development & Deployment Strategy Guide data scientists on: Model selection and evaluation strategies Experiment tracking and reproducibility

Design MLOps frameworks for: CI/CD of ML models Model versioning and governance Monitoring drift, accuracy, and bias

4. Technology & Platform Governance Define standards for: Programming languages and frameworks Cloud vs on prem deployments Security, privacy, and compliance

Ensure adherence to data governance, regulatory, and risk controls (especially in BFSI) 5. Documentation & Best Practices Produce: High level architecture diagrams Low level design documents Non functional requirement specifications

Establish best practices and reusable design patterns Your Profile Essential Skills / Knowledge / Experience Data Science & ML Supervised and unsupervised learning Time series, NLP, recommendation systems (as applicable) Programming Python (NumPy, Pandas, Scikit learn) Optional: R, SQL Data Platforms Relational & NoSQL databases Big data frameworks (Spark, Hive, Databricks) MLOps & Deployment Model lifecycle management CI/CD xehkeey pipelines Containerization (Docker, Kubernetes desirable) Model packaging and REST APIs Cloud & Tools (Any combination) AWS / Azure / GCP analytics and ML services MLflow, Azure ML, SageMaker, Vertex AI Version control (Git) Domain & Soft Skills Strong analytical and problem solving skills Ability to explain complex data science concepts in simple business language Experience working in Agile / Scrum environments Stakeholder management and decision facilitation Preferred Qualifications BFSI domain experience (risk, fraud, AML, credit, customer analytics) Experience with regulatory data modelling and explainable AI (XAI) Exposure to GenAI, LLMs, and vector databases Desirable Skills / Knowledge / Experience TOGAF or cloud architecture certifications