Data Scientist

Smart Spaces
London
2 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist- Consumer Behaviour

Lead Data Scientist

Data Scientist - Remote

Preferable: 6+ YOE Salary Range: £60,000-90,000 depending on experience Company DescriptionSmart Spaces is an award-winning, industry-leading white-label IoT platform, providing an all-in-one solution for building management systems control and communication. Our platform smart-enables workplaces, adding efficiency to daily work life for occupiers, employees, property owners, and managers. Our IoT application helps manage everything in your building, from granting access, system controls, and energy efficiency reports, to room booking and maintenance queries.Role DescriptionThis role is a great opportunity for a data-driven leader to get involved with all aspects of managing our data, from engineering to analytics to AI product development. We are looking for someone who thrives in an autonomous environment, can manage their product roadmap, and enjoys communicating with customers to understand their needs, and architect solutions that allow them to realise their goals.ResponsibilitiesLead an agile Data Science & Analytics team to spearhead the company's data & AI strategyDevelop & own the Data & AI product roadmap - by researching, prototyping, and implementing solutions to business challenges, from concept to productionDesign & implement ETL data pipelines to serve data for reporting & analytics, transforming sensor & operational data & calculating business metricsCreate interactive dashboards & visualisations to provide insights from our broad datasets, including data such as building occupancy, energy, & air qualityWork with customers to understand their data & reporting requirements, effectively communicate these to stakeholders, and develop product solutionsCollaborate with cross-functional teams to integrate solutions & align with broader product and company goals.Required Skills & ExperienceProgramming: Proficient in at least one language with a strong knowledge of OOP concepts (Python or C# preferred)Experience with SCM (Git), & DevOps concepts such as CI/CDData Engineering: Experience designing and implementing ETL pipelines, transforming & cleaning dataData & API's: Strong experience working with databases & API's, with experience guiding data architecture decisionsData Visualization & Reporting: Experience developing reporting dashboards, conducting analytics, communicating findingsExperience with BI tools beneficialAI Development: Knowledge of AI tools and AI application development, keen interest to learn moreExperience working with GenAI API's for product development preferredProduct Management & Stakeholder Engagement: Comfortable with product management tasks, including leading client calls, developing requirements, managing a product roadmapManagement: Self-motivated with good project management skills to manage your own time & that of your team within an Agile/Sprint frameworkBenefitsHybrid role with three days in-office expectationPrivate health insuranceCompany pension schemeDiscounts and Offers PlatformLearning and Development scheme

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.

Tips for Staying Inspired: How Data Science Pros Fuel Creativity and Innovation

Data science sits at the dynamic intersection of statistics, computer science, and domain expertise, driving powerful innovations in industries ranging from healthcare to finance, and from retail to robotics. Yet, the daily reality for many data scientists can be a far cry from starry-eyed talk of AI and machine learning transformations. Instead, it often involves endless data wrangling, model tuning, and scrutiny over metrics. Maintaining a sense of creativity in this environment can be an uphill battle. So, how do successful data scientists continue to dream big and innovate, even when dealing with the nitty-gritty of data pipelines, debugging code, or explaining results to stakeholders? Below, we outline ten practical strategies to help data analysts, machine learning engineers, and research scientists stay inspired and push their ideas further. Whether you’re just starting out or looking to reinvigorate a long-standing career, these pointers can help you find fresh sparks of motivation.

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.