Data Scientist

Bluetownonline
Manchester
2 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist (eDV clearance required)

Data Scientist - Remote

Job Title:Data Scientist

Location:Manchester

Salary:£46,148 to £60,809 DOE

Job type:Full Time, Permanent

Job Summary:

We are seeking a talented and highly motivated Data Scientist to join our team at the NHD. As a Data Scientist, you will play a key role in analysing and interpreting complex healthcare data to drive evidence-based decision-making and improve patient outcomes.

We have an exciting full-time opportunity available for an experience and promising Data Scientist at the National Haemophilia Database (NHD), to play a key role in analysing and interpreting complex healthcare data to drive evidence-based decision-making and improve patient outcomes.

The (NHD) is a register of people in the UK with all types of bleeding disorders started in 1969. Its purpose is to study the complete national cohort of patients with these conditions and improve the care of people with bleeding disorders. The database is held within the NHS and managed by the UK Haemophilia Centre Doctors’ Organisation (UKHCDO) which is an association of medical practitioners who work within the NHS Haemophilia Centre’s of England, Scotland, Northern Ireland, or Wales and have an interest in the care of people with Haemophilia or other inherited bleeding disorders.

NHD is at a transformation stage in its development and requires insight professionals to harness the wealth of data that is available to it. The NHD has the richest data store for bleeding disorders in the world and are investing heavily in the infrastructure required to enhance the processing, accessibility and surfacing of that data. What we now require is an individual that can harness the power of that data and convert it into insights and information that can make a real difference to the lives of people with bleeding disorders:

You would be working with the UKs best clinical, scientific, and statistical capabilities in this sector, whilst managing a small team. Would you enjoy being part of an NHD team that values, recognises, and celebrates staff members, their skills, and contributions? Could you play an invaluable part in a team to provide a high-level service to the NHS, work in partnership with the pharmaceutical sector who research and produce the current and future products for people with bleeding disorders, and ultimately, working with these partners, improve the lives of people with bleeding disorders? If yes, the National Haemophilia Database is the place for you.

Responsibilities:

Data Analysis: Perform data exploration, cleaning, and analysis on large healthcare datasets to derive meaningful insights and identify patterns. Predictive Modelling: Develop and implement predictive models to forecast patient outcomes, disease trends, and resource utilisation. Data Visualisation: Create visually compelling and easy-to-understand data visualisations to communicate findings and support data-driven decisions. Machine Learning: Utilise machine learning techniques to develop algorithms and models for various healthcare applications. Data Integration: Integrate disparate data sources to build comprehensive and holistic healthcare datasets for analysis. Collaborate with Healthcare Professionals: Work closely with clinicians and healthcare professionals to understand their data needs and provide data-driven solutions. Research Support: Assist in research projects by providing data expertise, statistical analysis, and interpretation of results. Continuous Improvement: Stay updated with the latest data science methodologies, tools, and technologies, and propose innovative approaches to enhance data analysis capabilities.

Requirements:

Education: Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or a related field. Experience: Proven experience as a Data Scientist in the healthcare domain, preferably within the NHS or a healthcare setting. Technical Skills: Proficiency in programming languages such as Python, R, or SQL, and experience with data manipulation and analysis tools. Statistical Knowledge: Strong statistical and quantitative analysis skills, including experience with statistical modelling and hypothesis testing. Data Visualisation: Ability to create interactive and informative data visualisations using tools such as Tableau or Power BI. Machine Learning: Familiarity with machine learning algorithms and frameworks for classification, regression, and clustering tasks. Communication: Excellent communication and presentation skills to effectively convey complex data insights to non-technical stakeholders.

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.

Careers

Tips for Staying Inspired: How Machine Learning Pros Fuel Creativity and Innovation

Machine learning (ML) continues to reshape industries—from personalised e-commerce recommendations and autonomous vehicles to advanced healthcare diagnostics and predictive maintenance in manufacturing. Yet behind every revolutionary model lies a challenging and sometimes repetitive process: data cleaning, hyperparameter tuning, infrastructure management, stakeholder communications, and constant performance monitoring. It’s no wonder many ML professionals can experience creative fatigue or get stuck in the daily grind. So, how do machine learning experts keep their spark alive and continually generate fresh ideas? Below, you’ll find ten actionable strategies that successful ML engineers, data scientists, and research scientists use to stay innovative and push boundaries. Whether you’re an experienced practitioner or just breaking into the field, these tips can help you fuel creativity and discover new angles for solving complex problems.

Careers

Top 10 Machine Learning Career Myths Debunked: Key Facts for Aspiring Professionals

Machine learning (ML) has become one of the hottest fields in technology—touching everything from recommendation engines and self-driving cars to language translation and healthcare diagnostics. The immense potential of ML, combined with attractive compensation packages and high-profile success stories, has spurred countless professionals and students to explore this career path. Yet, despite the boom in demand and innovation, machine learning is not exempt from myths and misconceptions. At MachineLearningJobs.co.uk, we’ve had front-row seats to the real-life career journeys and hiring needs in this field. We see, time and again, that outdated assumptions—like needing a PhD from a top university or that ML is purely about deep neural networks—can mislead new entrants and even deter seasoned professionals from making a successful transition. If you’re curious about a career in machine learning or looking to take your existing ML expertise to the next level, this article is for you. Below, we debunk 10 of the most persistent myths about machine learning careers and offer a clear-eyed view of the essential skills, opportunities, and realistic paths forward. By the end, you’ll be better equipped to make informed decisions about your future in this dynamic and rewarding domain.

Careers

Global vs. Local: Comparing the UK Machine Learning Job Market to International Landscapes

How to evaluate opportunities, salaries, and work culture in machine learning across the UK, the US, Europe, and Asia Machine learning (ML) has rapidly transcended the research labs of academia to become a foundational pillar of modern technology. From recommendation engines and autonomous vehicles to fraud detection and personalised healthcare, machine learning techniques are increasingly ubiquitous, transforming how organisations operate. This surge in applications has fuelled an extraordinary global demand for ML professionals—data scientists, ML engineers, research scientists, and more. In this article, we’ll examine how the UK machine learning job market compares to prominent international hubs, including the United States, Europe, and Asia. We’ll explore hiring trends, salary ranges, workplace cultures, and the nuances of remote and overseas roles. Whether you’re a fresh graduate aiming to break into the field, a software engineer with an ML specialisation, or a seasoned professional seeking your next challenge, understanding the global ML landscape is essential for making an informed career move. By the end of this overview, you’ll be equipped with insights into which regions offer the best blend of salaries, work-life balance, and cutting-edge projects—plus practical tips on how to succeed in a domain that’s constantly evolving. Let’s dive in.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.