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.