Data & AI Solution Architect

OBSS
Sheffield
2 days ago
Applications closed

Related Jobs

View all jobs

Data & AI Solution Architect

Wealth & Asset Management AI & Data - Partner (Non Equity)

SAP BTP Business AI Engineer

Data & AI Architect, Microsoft Azure, PaaS, ETL, Data Modelling Remote

Data & AI Architect, Microsoft Azure, PaaS, ETL, Data Modelling Remote

Market Facing Director - Databricks - AI & Data

About Us

At OBSS Technology, we empower organizations to achieve their goals through innovative, data-driven, and AI-focused solutions. With expertise in large-scale enterprise projects, we leverage cutting-edge technologies to transform business processes and deliver exceptional value. We are seeking an experienced Data & AI Solution Architect to join our team and spearhead transformative retail and e-commerce projects.


Role Overview

As a Data & AI Solution Architect, you will drive the design and delivery of cutting-edge solutions that align with our clients' business strategies. You will oversee the end-to-end technical roadmap, lead architectural decisions, and ensure successful project execution. This role will focus on delivering impactful data and AI solutions, integrating modern machine learning techniques, and driving innovation within the retail and e-commerce domain.


Responsibilities

• Drive the design and implementation of large-scale data architectures and analytics projects for a retail/e-commerce client.

• Incorporate advanced Data Science and AI capabilities such as Natural Language Processing (NLP), Computer Vision, Generative AI (GenAI) and LLM into client projects.

• Translate complex business requirements into technical solutions, ensuring seamless integration into existing systems.

Qualifications

• Bachelor’s or higher degree in a relevant field such as Computer Science, Software Engineering, Data Science, Mathematics, Statistics, or related disciplines.

• Minimum 8 years of experience in Data Science, Machine Learning, Deep Learning and Advanced Data Analytics projects.

• Hands-on experience with modern data and ML platforms, cloud AI/Data services, major open source tools and libraries.

• Experience with MLOps practices (e.g., MLflow, Kubeflow) is a plus.

• Strong communication skills for translating technical concepts to non-technical stakeholders.

• Experience in a number of Retail, CRM, Marketing use cases (e.g., advanced personalization, recommendation systems, segmentation, campaign targeting, CLV, churn analysis) is required.

• Having industry certifications in AI, data analytics, or data science is a plus.

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Top 10 Data Science Career Myths Debunked: Key Facts for Aspiring Professionals

Data science has become one of the most sought-after fields in the tech world, promising attractive salaries, cutting-edge projects, and the opportunity to shape decision-making in virtually every industry. From e-commerce recommendation engines to AI-powered medical diagnostics, data scientists are the force behind innovations that drive productivity and improve people’s lives. Yet, despite the demand and glamour often associated with this discipline, data science is also shrouded in misconceptions. Some believe you need a PhD in mathematics or statistics; others assume data science is exclusively about machine learning or coding. At DataScience-Jobs.co.uk, we’ve encountered a wide array of myths that can discourage talented individuals or mislead those exploring a data science career. This article aims to bust the top 10 data science career myths—providing clarity on what data scientists actually do and illuminating the true diversity and inclusiveness of this exciting field. Whether you’re a recent graduate, a professional looking to pivot, or simply curious about data science, read on to discover the reality behind the myths.

Global vs. Local: Comparing the UK Data Science Job Market to International Landscapes

How to evaluate salaries, opportunities, and work culture in data science across the UK, the US, Europe, and Asia Data science has proven to be more than a passing trend; it is now a foundational pillar of modern decision-making in virtually every industry—from healthcare and finance to retail and entertainment. As the volume of data grows exponentially, organisations urgently need professionals who can transform raw information into actionable insights. This high demand has sparked a wave of new opportunities for data scientists worldwide. In this article, we’ll compare the UK data science job market to those in the United States, Europe, and Asia. We’ll explore hiring trends, salary benchmarks, and cultural nuances to help you decide whether to focus your career locally or consider opportunities overseas or in fully remote roles. Whether you’re a fresh graduate looking for your first data science position, an experienced data professional pivoting from analytics, or a software engineer eager to break into machine learning, understanding the global data science landscape can be a game-changer. By the end of this overview, you’ll be better equipped to navigate the expanding world of data science—knowing which skills and certifications matter most, how salaries differ between regions, and what to expect from distinct work cultures. Let’s dive in.

Data Science Leadership for Managers: Strategies to Motivate, Mentor, and Set Realistic Goals in Data-Driven Teams

Data science has become a linchpin in modern business, transforming oceans of raw data into actionable insights that guide strategy, product development, and personalised customer experiences. With this surge in data-centric operations, the need for effective data science leadership has never been more critical. Guiding a team of data scientists, analysts, and machine learning engineers requires not only technical acumen but also the ability to foster collaboration, champion ethical practices, and align complex modelling efforts with overarching business goals. This article provides practical guidance for managers and aspiring leaders aiming to excel in data-driven environments. By exploring strategies to motivate data science professionals, develop mentoring frameworks, and set achievable milestones, you will be better prepared to steer your team towards meaningful, evidence-based outcomes.