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- Tasks: Design scalable data monitoring systems and troubleshoot anomalies.
- Company: Join a forward-thinking data company delivering insights for top global brands.
- Benefits: Enjoy a hybrid work model with flexibility between office and remote work.
- Why this job: Be part of a culture that values autonomy, curiosity, and impactful data solutions.
- Qualifications: Strong Python and SQL skills; experience with large datasets and cloud infrastructure.
- Other info: Opportunity to mentor junior colleagues and contribute to innovative frameworks.
The predicted salary is between 36000 - 60000 £ per year.
Our client is an industry leading market-research company who use data-driven insights to provide unique and actionable data for some of the world\’s most recognised brands. Over the last 12 months they\’ve experienced a period of rapid growth and they are positioned to continue this growth for the foreseeable future so it\’s an ideal time to join the team.
They need a strong Data Scientist to design, develop and deploy systems to monitor data health.
Key Responsibilities:
- Design and implement monitoring and alerting systems to ensure the reliability and accuracy of key datasets and processes.
- Collaborate with teams to define relevant metrics, thresholds, and KPIs.
- Build, maintain, and productionise machine learning and statistical models using Python and PySpark.
- Design and implement automation tools which can help dynamically adapt our products to external changes
- Integrate LLM tooling into pipelines to aid with automation
- Deploy monitoring tools and models using AWS infrastructure.
- Investigate and troubleshoot anomalies in the data pipeline.
- Promote data quality and monitoring best practices across the business.
- Contribute to a culture of curiosity, rigour, and innovation.
- Apply automation and AI-assisted tools where appropriate to improve delivery efficiency and the quality of analytical outputs.
Skills Needed:
- Proficiency in Python and SQL for analysis, model development, and data interrogation.
- Comfortable deploying statistical or ML models into production environments.
- Strong understanding of cloud infrastructure, preferably AWS.
- A methodical, problem-solving mindset with high attention to detail.
- Able to scope, define, and deliver complex solutions independently.
- Comfortable working closely with non-technical stakeholders to define business-critical metrics.
- Self-motivated, accountable, and keen to continuously learn and grow.
- Previous experience building monitoring or data quality frameworks is highly desirable.
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Data Scientist employer: SearchWorks
Contact Detail:
SearchWorks Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist
✨Tip Number 1
Familiarise yourself with the latest trends in data monitoring and quality frameworks. Being able to discuss recent advancements or tools during your interview can demonstrate your proactive approach and genuine interest in the field.
✨Tip Number 2
Network with current or former employees of the company on platforms like LinkedIn. Engaging in conversations about their experiences can provide you with valuable insights into the company culture and expectations, which you can leverage during your discussions.
✨Tip Number 3
Prepare to showcase your problem-solving skills by thinking of specific examples where you've successfully tackled data integrity issues or built scalable systems. This will help you illustrate your hands-on experience and ability to deliver independently.
✨Tip Number 4
Brush up on your Python and SQL skills, especially focusing on how they apply to large datasets and distributed processing. Being able to confidently discuss your technical expertise will set you apart from other candidates.
We think you need these skills to ace Data Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your strong Python and SQL skills, as well as your experience with large datasets and distributed processing. Use specific examples from your past work that demonstrate your ability to design and implement scalable systems.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Mention how your proactive and detail-oriented mindset aligns with their needs, and provide examples of how you've successfully owned projects end-to-end in the past.
Showcase Relevant Projects: If you have experience building monitoring or data quality frameworks, make sure to include this in your application. Highlight any projects where you deployed models into production environments, especially using AWS services.
Prepare for Technical Questions: Be ready to discuss your technical skills in Python, SQL, and PySpark during the interview process. Prepare to explain how you've tackled data anomalies and contributed to data integrity in previous roles.
How to prepare for a job interview at SearchWorks
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Python, SQL, and PySpark in detail. Bring examples of projects where you've successfully implemented these technologies, especially in relation to data integrity and monitoring.
✨Demonstrate Problem-Solving Abilities
Expect to be asked about how you approach troubleshooting data anomalies. Prepare a few scenarios where you identified a problem, defined the solution, and successfully deployed it, highlighting your end-to-end project ownership.
✨Highlight Collaboration Experience
Since the role involves working across teams, share examples of how you've collaborated with both technical and non-technical colleagues. Emphasise your ability to define metrics and thresholds in a way that everyone can understand.
✨Discuss Your Mentoring Approach
As mentoring junior colleagues is part of the role, think about how you would support their growth. Be ready to talk about your experiences in mentoring or leading teams, and how you foster a culture of curiosity and rigor.
