Senior Data Scientist (Clinical Programming)

Ascent
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist/ Senior Risk Scientist

Senior Data Scientist (MLOps)

Senior Data Scientist (GenAI)

Senior Data Engineer

Lead Machine Learning Engineer

Lead Data Scientist

This is a perfect opportunity for ambitious individuals to join a team of curious minds and support peers with a passion and the skills for creating value for businesses using data.


About us

We are Ascent! and we help our customers solve problems, elevate, and do existing things better. We are on a mission to help our customers connect data, software, and purpose to create extraordinary outcomes. You could say we are a digital transformation business.

We specialize in software product development, analytics, data science, IoT solutions, machine learning, DevOps optimization, and modernization of applications, data, and platforms.

We work with incredible clients in all types of industries such as smart home devices, space exploration, beer manufacturing, finance, ecology, and logistics. We work with some of the sharpest minds in the brightest businesses and we employ the sharpest minds too!

At Ascent, we also believe in fostering a vibrant office community where collaboration thrives and connections flourish. With our hybrid approach, we prioritize hiring individuals who reside in close proximity to our central offices in Bristol and London. Our aim is to cultivate a positive atmosphere and sense of belonging by facilitating easy access to the office. Join us in shaping a workplace where proximity enhances collaboration while inclusivity strengthens our community.


About the role

We are looking for an experienced Shiny developer to work at one of our global re/insurance customers on a range of Shiny optimisation consulting and development engagements. They are expected to bring extensive knowledge in best practices on Shiny design, Shiny performance management, collaboration with version control, release strategy, user access control, and familiarity with other topics such as data architecture, schema modelling and cloud infrastructure. They will be working closely with the customer; this demands professionalism and empathy. A dedicated focus on delivering high-impact outcomes that delight our customers is key.

A successful candidate will have demonstrable experience in enterprise and production-grade Shiny development, including but not limited to:

  1. Shiny application performance enhancement
  2. Code reviews and optimisation
  3. Asynchronous algorithm API integration
  4. Advisory on UI design and development of complex UI elements
  5. OAuth authentication integration
  6. Deployment to cloud
  7. Testing framework

Skills and Experience

A successful candidate will demonstrate these traits:

  1. Experienced with clinical reporting, having worked within pharma or clinical research organisations
  2. Strong R and R package development
  3. CDSIC standard integration
  4. Nice to have R Shiny
  5. Worked directly with cloud infrastructure (ideally Azure)
  6. Understand data modelling (not machine learning modelling)
  7. Can work with customer stakeholders to understand business processes and workflows, and can design solutions to optimize processes via streamlining and automation
  8. DevOps experience and familiarity with app release process
  9. Familiar with agile delivery methods

Working at Ascent

At Ascent we promote a healthy work-life balance by offering flexibility in where you work. We also promote well-being and provide access to Well Being Coaches.

Your development and learning will be taken seriously, and we'll support your professional development with training and certification, with regular feedback and review. It is a fun, supportive and modern workplace where we really live by our company values of Empathy, Energy and Audacity! Ascent also offers a variety of benefits in each of our countries.

Ascent is an equal opportunities employer. We take intentional steps to ensure inclusion and belonging are something real here, not just something we talk about. No person will be treated less favourably because of their gender, pregnancy, and maternity status, marital or civil partnership status, sexual orientation, race, nationality, ethnic origin, age, religion or belief, or disability status. If you require any reasonable accommodation, please let us know when you apply.

If you have any questions contact our Talent Acquisition team on .

For more details aboutlife at Ascent, check out our Life Page here.

#J-18808-Ljbffr

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.