Software Engineer Full Stack - HealthTech Scale-Up

Sheldon Square
3 months ago
Applications closed

Related Jobs

View all jobs

Technical Lead Software Engineer

Software Engineer

IT Technical Lead

Software Engineers

Graduate Manufacturing Support Engineer

IT and Web Engineer

Software Engineer / Developer (TypeScript React AWS) London / WFH to £80k

Opportunity to join a highly successful and scaling Tech for Good company within the fertility and IVF space that helps to streamline treatment options, assist with financing and provide holistic patient care.

As a Software Engineer you'll focus on building the financial platforms for the insurance and lending products, which aim to lessen the financial burden and stress of fertility treatment. You'll be working with a modern tech stack that you can influence, to deliver innovative features quickly and efficiently, making effective trade-offs that consider business priorities, user experience and technical feasibility, working in close collaboration with product, design, ops and data teams.

What's in it for you:

Salary to £80k
Equity
Vitality Healthcare
Pension
Fertility Support
Workplace nursery benefit
£1,000 annual training budget
Hybrid working (x3 office per week)
Inclusive environment with great DEI, women are well represented at C-Suite level and within the data engineering teamAbout you:

You have experience across the full technology stack, they're using JavaScript / TypeScript, PostgreSQL, AWS and React
You have experience of working in start-up / scaling technology companies
You enjoy collaborating and problem solving and are focussed on delivering business impact
You have a strong knowledge of modern software engineering best practices
You're happy to use a range of programming languages and explore new and emerging technology that could enhance the platformApply now to find out more about this Software Engineer / Developer (Full Stack TypeScript React AWS) opportunity.

At Client Server we believe in a diverse workplace that allows people to play to their strengths and continually learn. We're an equal opportunities employer whose people come from all walks of life and will never discriminate based on race, colour, religion, sex, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. The clients we work with share our values

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.

Shadowing and Mentorship in Data Science: Gaining Experience Before Your First Full-Time Role

How to Find Mentors, Build Industry Connections, and Hone the Skills Needed in a Fast-Evolving Field Introduction Over the past decade, data science has grown from a niche academic discipline to a pivotal function driving decision-making in businesses of all sizes. With an array of applications—from predictive analytics and natural language processing to recommender systems and computer vision—data science offers an enticing career path for analytically minded professionals. However, as the field expands, so too does the level of competition. Employers seek not just theoretical knowledge but also real-world experience and robust problem-solving skills. That’s where shadowing and mentorship become game-changers for early-career data scientists. These hands-on learning opportunities provide an unmatched window into the workflows, tools, and soft skills you’ll need to excel in a professional environment. Whether you’re still completing your studies, fresh out of a bootcamp, or transitioning from another career, working closely with experienced data scientists can significantly shorten your learning curve and help you stand out when applying for your first full-time position. This article explores how to find mentors who align with your goals, the best ways to engage in shadowing opportunities, and practical tips for showcasing your growth as a mentee. From clarifying the nature of data science roles to leveraging online networks, you’ll discover how to position yourself as a candidate poised to solve complex challenges and drive data-driven innovation.

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.