At a Glance
- Tasks: Lead data management projects and conduct complex analyses for nature recovery.
- Company: Join a passionate national team at Natural England focused on quality technical delivery.
- Benefits: Enjoy competitive salary, flexible working, and opportunities for professional growth.
- Other info: Dynamic role with excellent career advancement opportunities in a supportive environment.
- Why this job: Make a real impact on environmental projects using innovative data science techniques.
- Qualifications: Experience in data analysis, project leadership, and coding in Python or R.
The predicted salary is between 54000 - 66000 £ per year.
Data Science Services (DSS) is part of the Analysis Directorate.
We are a large, national team and have broad skills including data science, analysis and statistics.
We are passionate about quality technical delivery and continuous improvement, using novel data science approaches to help everyone understand nature recovery.
The roles would be initially based in our Marine or terrestrial geospatial analysis and data management team with potential to lead on data management projects, including standardisation, metadata and licensing.
- Lead and oversee standard data and spatial analyses, carry out standard and complex analyses, to meet the evidence needs of a variety of Natural England, Defra group and other stakeholders
- Ensure project data standards, processes and systems are followed to ensure outputs are high-quality and robust (including through good documentation and automation) so that customer decisions and advice can be based on sound evidence and outputs can be reproduced.
- For projects where the role is leading, liaise with project managers and teams to establish project planning and resourcing so that projects are delivered efficiently, with appropriate resource and capability, to a high standard, and progress, risks and issues can be monitored.
- People leadership skills / experience in either a functional or line management context (Essential)
- Experience of planning and leading the delivery of projects (Essential)
• Data analysis skills including
- Data extraction/ingestion and preparation, exploratory data analysis, and data visualisation (Essential)
- Working with a range of large scale spatial and non-spatial data types in both structured and unstructured formats (Essential)
- Experience working with (preferably combining) a range of biological, geographical and social data (Essential)
- Experience of coding in Python, R and FME (Desirable)
- Experience in Machine Learning / Deep Learning applications (Desirable)
- Experience in GIS software (e. g. Arc Pro, Arc GIS, Arc GIS Online, QGIS) (Essential)
- Knowledge of relevant data/metadata standards (Essential)
- Understanding of good practice, ethics, and quality assurance for data analysis (Essential)
- Working with databases, web-based applications, open-source software and APIs (Essential)
- Experience of a range of statistical techniques and applications (e. g.
SAS, SPSS, R) for identifying relationships between data and for inference, parametric and non‑parametric analysis and methods for quantifying uncertainty. (Essential)
- Experience in designing or contributing to digital tools such as data‑collection apps, dashboards, or field‑data interfaces (Desirable)
- Technical/specialist knowledge
- Problem solving (Essential)
• Data analysis skills including
- Data extraction/ingestion and preparation, exploratory data analysis, and data visualisation (Essential)
- Working with a range of spatial and non-spatial data types (Essential)
- Experience working with biological, geographical and social data (Essential)
- Experience in geospatial software (e. g. Arc Pro, Arc GIS, Arc GIS Online, QGIS) (Essential)
- Knowledge of relevant data/metadata standards (Essential)
- Understanding of good practice, ethics, and quality assurance for data analysis (Essential)
- Experience of coding in R/Python, including use of Git Hub for robust version control (Desirable)
- Working with databases, web-based applications and APIs (Desirable)
- Experience of a range of statistical techniques and applications. (Essential)
- Understanding of habitat data and classification approaches in a UK context (Desirable)
- Handle data responsibly, understand your role in good governance and comply with delegations, policies and procedures (Practitioner)
- Manage and delivery your work to meet agreed targets and deadlines (Practitioner)
- Personal Effectiveness
- Be ambitious, identify and adopt ways to make improvements in your team and Natural England and inspire others to act (Practitioner)
- Share expertise and knowledge with people and networks so that you can develop together (Practitioner)
- Apply new technology to improve the way we deliver our work (Practitioner)
- #J-18808-Ljbffr
Higher Data Scientist - ( Ref : 6726 ) in London employer: Natural England
Natural England is an excellent employer, offering a dynamic work environment where employees can contribute to vital ecological projects that impact the future of England's natural landscapes. With a strong emphasis on employee development and collaboration, team members benefit from opportunities for growth and learning while working alongside passionate professionals dedicated to conservation. The supportive culture and commitment to sustainability make this role not only meaningful but also rewarding for those looking to make a difference in the ecosystem.
StudySmarter Expert Advice🤫
We think this is how you could land Higher Data Scientist - ( Ref : 6726 ) in London
✨Get Involved in Data Science Meetups
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Natural England.
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We think you need these skills to ace Higher Data Scientist - ( Ref : 6726 ) in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Natural England, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Natural England. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Natural England
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Natural England!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
