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- Tasks: Analyze complex datasets, develop predictive models, and support decision-making.
- Company: Join a dynamic company in Maidenhead focused on data-driven solutions.
- Benefits: Enjoy a hybrid work model with 3 days onsite and 2 days remote.
- Why this job: Be part of a collaborative team that values innovation and continuous learning.
- Qualifications: Bachelor’s degree in a related field and 2+ years of experience required.
- Other info: Mentorship opportunities available for junior data scientists.
The predicted salary is between 36000 - 60000 £ per year.
Data Engineer – Maidenhead (Hybrid) – Permanent
I am looking for a Data Engineer / Data Scientist for a company in the Maidenhead area.
Work is Hybrid – 3 days onsite, 2 days remote. You will be leveraging your analytical skills and programming experience to extract insights from complex datasets, develop predictive models and support decision making.
KEY RESPONSIBILITIES
- Data Analysis & Modelling: Analyse large, complex datasets to identify trends, patterns, and actionable insights.
- Develop, implement, and optimize machine learning models to solve business problems.
- Conduct A/B testing and experimental analysis to validate hypotheses.
Data Management & Engineering:
- Collaborate with data engineering teams to ensure data quality, accessibility, and efficiency.
- Design and develop ETL pipelines and workflows for data preprocessing.
- Develop automated tests to validate the processes and models you create.
Collaboration & Communication:
- Collaborate with stakeholders to define project goals, requirements, and deliverables.
- Actively participate in design meetings to help shape the solutions that the team delivers
- Present findings and recommendations to technical and non-technical audiences.
- Acquire domain knowledge to inform modelling opportunities and model feature creation
Technical Leadership:
- Mentor junior data scientists and provide peer reviews for modelling projects.
- Stay current with industry trends, tools, and best practices to continuously improve the team\’s capabilities.
QUALIFICATIONS
Education:
- Bachelor’s degree in data science, Statistics, Mathematics, or a related field.
Experience:
- 2 or more years of experience in a data science or analytics role.
- Proven experience in building machine learning models, statistical analysis, and predictive analytics.
- Experience designing experiments or modelling approaches to solve a specified business problem.
PREFERRED QUALIFICATIONS
- Proficiency in programming languages such as Python or R; knowledge of is R an advantage.
- Experience with SQL and working knowledge of relational databases.
- Proficiency with data visualisation tools and techniques.
- Experience with AWS is a plus.
- Strong problem-solving and critical-thinking abilities.
- Excellent communication and presentation skills.
- Ability to manage multiple projects and prioritize tasks effectively.
If this exciting opportunity could be of interest – please let me know ASAP. Interviews can be arranged at short notice.
Data Engineer employer: WHD Consulting Ltd
Contact Detail:
WHD Consulting Ltd Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer
✨Tip Number 1
Make sure to showcase your experience with machine learning models and data analysis during the interview. Be prepared to discuss specific projects where you've successfully extracted insights from complex datasets.
✨Tip Number 2
Familiarize yourself with the latest trends in data engineering and analytics. Being able to discuss current tools and best practices will demonstrate your commitment to continuous improvement and technical leadership.
✨Tip Number 3
Practice explaining your technical work to non-technical audiences. Since communication is key in this role, being able to present your findings clearly will set you apart from other candidates.
✨Tip Number 4
If you have experience with AWS or data visualization tools, be sure to highlight that. These skills are preferred qualifications and can give you an edge in the selection process.
We think you need these skills to ace Data Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in data analysis, machine learning, and programming. Use keywords from the job description to demonstrate that you meet the qualifications.
Craft a Strong Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Mention specific projects or experiences that align with the responsibilities listed, such as developing ETL pipelines or conducting A/B testing.
Showcase Technical Skills: Clearly outline your proficiency in programming languages like Python or R, as well as your experience with SQL and data visualization tools. Providing examples of past projects can strengthen your application.
Prepare for Interviews: Be ready to discuss your analytical skills and problem-solving abilities. Prepare to explain your approach to data modeling and how you've collaborated with stakeholders in previous roles.
How to prepare for a job interview at WHD Consulting Ltd
✨Showcase Your Analytical Skills
Be prepared to discuss specific examples of how you've analyzed large datasets in the past. Highlight any trends or insights you discovered and how they impacted decision-making.
✨Demonstrate Your Technical Proficiency
Make sure to mention your experience with programming languages like Python or R, as well as your familiarity with SQL and data visualization tools. Be ready to discuss any machine learning models you've built and the outcomes they achieved.
✨Prepare for Collaboration Questions
Since collaboration is key in this role, think of examples where you've worked with stakeholders to define project goals. Be ready to explain how you communicated complex technical concepts to non-technical audiences.
✨Stay Current with Industry Trends
Research recent developments in data science and analytics. Being able to discuss current trends or tools will show your commitment to continuous improvement and your ability to bring fresh ideas to the team.
