We particularly welcome applications from BAME communities, people with disabilities and/or long-term health conditions and LGBT+ community members.
We have policies and procedures in place to ensure that all applicants and employees are treated fairly and consistently. We are proud to be accredited as a Disability Confident Employer, a member of Business Disability Forum and a Stonewall Diversity Champion.
We have active and Executive supported BAME, LGBT+ and Allies, Disability and Long-Term Health Conditions and Women’s staff networks. Staff networks are employee-led groups formed around interests, issues and a common bond or background. Staff network members create a positive and inclusive work environment at Great Ormond Street Hospital by actively contributing to the Trust’s mission, values and efforts specific to inclusion. All of our staff networks are open to any employee.
Job description
Job responsibilities
Analytics
- Develop, run and maintain analytics pipelines and data solutions to support clinical and operational research in the Trust;
- Engage with the partner hospitals and stakeholders including partner project teams and operational experts to understand analytical aspects of business problems and identify how data can be used to help solve these problems;
- Liaise with collaborators to optimise the use of GOSHs research infrastructure for data analytics/ science tasks;
- Provide help and guidance to researchers in the hospital as part of DRE Open Support sessions, mainly advising on data wrangling, analysis and statistical interpretation;
- Provide assistance in the ongoing development and documentation utilising GOSHs OHDSI OMOP data, and ongoing sustainability goals of Common Data Model (CDM) use in the NHS;
- Manipulate data and/or run and collate reports or other documentation as required for DRE management and the groups that oversee the DRE;
- Develop solutions that are consistent with and complement development work across the data professionals in DRIVE;
Person Specification
Academic/Professional qualification/Training
Essential
- Educated to degree level in a computer science, mathematical, science-based or relevant subject
- Maths and English GCSE grade C or above or equivalent qualification
- IT qualification in MS Office or equivalent experience
GOSH Culture and Values
Essential
- Our Always values ? Always welcoming ? Always helpful ? Always expert ? Always one team
Academic/Professional qualification/Training
Essential
- Post graduate qualification or equivalent relevant experience
Desirable
- Project management foundation level or relevant experience
Experience/Knowledge
Essential
- Experience of programming tools such as R, Python, SQL for analytics and dashboarding
- Sound knowledge of operational workflows and processes within a hospital environment
- Experience working under a tight information governance regime including strict separation of resources and the management of Personally Identifiable Information/other sensitive data
- Experience of working with teams to operationalise data driven solutions
- Knowledge of medical terminology
- Experience working with healthcare-specific data models in data transformation and analyses (e.g. transformation into OMOP)
Desirable
- Experience of project management and/or version control (e.g. git, agile development, issue tracking)
- Experience of using data analytics in academic research projects
- Experience of federated analytics in a health care setting
Skills/Abilities
Essential
- Excellent organisational and planning skills, including prioritising to achieve deadlines under pressure and with frequent interruptions
- Excellent written, verbal and presentation skills with the ability to communicate complex information to staff at all levels
Desirable
- Skills in developing dashboards/interactive data summaries
GOSH Culture and Values
Essential
- Experience of working as part of a diverse team.
- Experience of contributing to an inclusive workplace culture.
- Knowledge and understanding of diverse backgrounds and perspectives.
- Understanding of Diversity and Inclusion challenges in the workplace.
- Demonstrable contribution to advancing Equality, Diversity and Inclusion in the Workplace
Disclosure and Barring Service Check
This post is subject to the Rehabilitation of Offenders Act (Exceptions Order) 1975 and as such it will be necessary for a submission for Disclosure to be made to the Disclosure and Barring Service (formerly known as CRB) to check for any previous criminal convictions.
Applications from job seekers who require current Skilled worker sponsorship to work in the UK are welcome and will be considered alongside all other applications. For further information visit the UK Visas and Immigration website (Opens in a new tab).
From 6 April 2017, skilled worker applicants, applying for entry clearance into the UK, have had to present a criminal record certificate from each country they have resided continuously or cumulatively for 12 months or more in the past 10 years. Adult dependants (over 18 years old) are also subject to this requirement. Guidance can be found here Criminal records checks for overseas applicants (Opens in a new tab).
Here are some frequently asked questions (FAQs) about the role of a data scientist:
1. What is a data scientist?
A data scientist is a professional who uses scientific methods, processes, algorithms, and systems to extract insights and knowledge from structured and unstructured data. They apply statistical analysis, machine learning, and data mining techniques to solve complex business problems.
2. What skills are required to become a data scientist?
Some of the key skills needed for a data scientist include:
- Programming: Proficiency in languages like Python, R, and SQL.
- Statistical analysis: Knowledge of statistics and probability.
- Machine learning: Understanding of algorithms and model-building techniques.
- Data visualization: Ability to communicate findings using tools like Tableau, Power BI, or matplotlib.
- Data wrangling: Experience in cleaning and preparing data.
- Big Data technologies: Familiarity with Hadoop, Spark, or similar tools can be beneficial.
3. What are the primary responsibilities of a data scientist?
A data scientist’s primary responsibilities include:
- Collecting and preprocessing data.
- Analyzing large datasets to uncover patterns and insights.
- Building and validating predictive models using machine learning.
- Communicating findings and recommendations to stakeholders.
- Designing experiments or algorithms to optimize decision-making.
4. What is the difference between a data scientist and a data analyst?
While both roles involve working with data, data scientists focus more on building advanced models and applying machine learning techniques, while data analysts primarily interpret data and generate reports. Data scientists also typically work with larger, more complex datasets.
5. What industries employ data scientists?
Data scientists are employed across many industries, including:
- Technology and software companies
- Finance and banking
- Healthcare and pharmaceuticals
- Retail and e-commerce
- Government and public policy
- Marketing and advertising
6. What is the typical education required for a data scientist?
Most data scientists have a background in fields such as computer science, statistics, mathematics, or engineering. A bachelor’s degree is often required, but many roles also require a master’s degree or PhD in a relevant field. Some data scientists also pursue certifications or online courses to further develop their skills.
7. What tools and technologies do data scientists use?
Common tools and technologies for data scientists include:
- Programming languages: Python, R, SQL
- Data manipulation libraries: pandas, NumPy
- Machine learning frameworks: scikit-learn, TensorFlow, PyTorch
- Big data tools: Hadoop, Spark
- Visualization tools: Tableau, Power BI, matplotlib, Seaborn
- Databases: MySQL, PostgreSQL, MongoDB
8. What is the typical career path for a data scientist?
A data scientist’s career can progress in various ways, such as:
- Junior/Entry-Level Data Scientist: Building foundational skills and gaining experience.
- Mid-Level Data Scientist: Managing larger projects and working more independently.
- Senior Data Scientist/Lead Data Scientist: Leading teams, mentoring others, and working on complex business problems.
- Data Science Manager or Director: Overseeing data science teams and contributing to strategic business decisions.
9. What is the average salary for a data scientist?
Salaries for data scientists vary depending on experience, location, and industry. In the U.S., the average salary for a data scientist ranges from $85,000 to $130,000 annually. Senior-level data scientists or those in high-demand locations can earn much more, sometimes exceeding $150,000 or more.
10. How does a data scientist contribute to business decision-making?
Data scientists help businesses make data-driven decisions by:
- Analyzing trends and patterns in data.
- Building predictive models to forecast outcomes.
- Optimizing business operations through data insights.
- Creating recommendation systems to personalize customer experiences.
- Identifying new opportunities and improving efficiency.
11. What challenges do data scientists face?
Some common challenges faced by data scientists include:
- Data quality: Ensuring data is clean, accurate, and relevant.
- Communication: Translating complex technical findings into actionable insights for non-technical stakeholders.
- Model interpretability: Ensuring that machine learning models are understandable and transparent.
- Data privacy and ethics: Navigating the ethical considerations of using sensitive data.
12. How can one become a data scientist?
To become a data scientist, you should:
- Gain proficiency in programming and statistics.
- Learn machine learning techniques and tools.
- Build a portfolio with data projects to showcase your skills.
- Consider getting a relevant degree or certification.
- Gain practical experience through internships or real-world projects.
13. Is data science a good career choice?
Yes, data science is considered a highly rewarding career due to the growing demand for data-driven decision-making across industries, the variety of challenging problems to solve, and the high earning potential. However, it requires continuous learning and adapting to new technologies and methodologies.
