Data Solution Designer Data Science
Role / Job Title:Data Solution Designer Data ScienceWork Location:Norwich 3 Days (Flexible)Duration of Assignment:06 MonthsThe RoleThe 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 ResponsibilitiesSolution & Data Model Design1. 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
- 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
- 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)
- Produce:
- High level architecture diagrams
- Low level design documents
- Non functional requirement specifications
- Establish best practices and reusable design patterns
- Supervised and unsupervised learning
- Time series, NLP, recommendation systems (as applicable)
- Python (NumPy, Pandas, Scikit learn)
- Optional: R, SQL
- Relational & NoSQL databases
- Big data frameworks (Spark, Hive, Databricks)
- Model lifecycle management
- CI/CD pipelines
- Containerization (Docker, Kubernetes desirable)
- Model packaging and REST APIs
- AWS / Azure / GCP analytics and ML services
- MLflow, Azure ML, SageMaker, Vertex AI
- Version control (Git)
- 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
- 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
- TOGAF or cloud architecture certifications
JBRP1_UKTJ