Data Solution Designer Data Science

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 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

TPBN1_UKTJ