At a Glance
- Tasks: Lead a team in building data ingestion systems and mentor fellow engineers.
- Company: Join Reflection, a pioneering company shaping the future of AI.
- Benefits: Attractive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on innovation and scalability.
- Why this job: Make a significant impact on cutting-edge AI models and data delivery.
- Qualifications: Experience in data engineering and strong leadership skills.
The predicted salary is between 72000 - 88000 £ per year.
Reflection's Data team builds the training corpora our frontier models learn from.
As Data Ingestion Lead, you'll provide front-line leadership of the team that builds this layer, spanning all three pillars: web crawl, data ingestion pipelines, and data lakes.
You'll mentor engineers, guide architecture, and contribute as an individual when needed.
You will work closely with pre-training research, data quality, and partnerships to ensure scalable, auditable data delivery that directly affects
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Data Ingestion Engineering Lead — Scale & Impact in London employer: Reflection
At Reflection, we pride ourselves on being an exceptional employer that champions innovation and collaboration in the rapidly evolving field of AI. Our inclusive work culture fosters personal and professional growth, offering top-tier compensation, comprehensive health benefits, and unlimited paid time off to ensure a healthy work-life balance. Join us in shaping the future of open foundational models while enjoying unique perks like daily meals and generous parental leave policies, all within a dynamic and supportive environment.
StudySmarter Expert Advice🤫
We think this is how you could land Data Ingestion Engineering Lead — Scale & Impact 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 Reflection.
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We think you need these skills to ace Data Ingestion Engineering Lead — Scale & Impact 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 Reflection, 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 Reflection. 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 Reflection
✨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
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✨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.
