At a Glance
- Tasks: Design and scale data systems, build AI solutions, and partner with stakeholders.
- Company: Join IQ Capital, a leading deep tech venture capital firm in London.
- Benefits: High autonomy, direct access to partners, and impactful work environment.
- Other info: Work with cutting-edge technologies in a dynamic, low-bureaucracy setting.
- Why this job: Own the data and AI strategy that shapes investment decisions.
- Qualifications: 2-4 years in data or machine learning engineering, skilled in Python and SQL.
The predicted salary is between 80000 - 98000 £ per year.
IQ Capital is a London-based deep tech venture capital firm with approximately $1bn AUM, specializing in IP-rich sectors like AI, quantum computing, and biotech. With a portfolio of 60+ active companies and a lean team of 18, they provide high-conviction support to the founders shaping the next generation of technology.
Location: London, UK
Salary: Not disclosed
As a Product + Automation Engineer, you will own IQ Capital’s core data foundations and AI workflows. Sitting at the heart of the investment platform, you'll design intelligent systems for deal sourcing, portfolio monitoring, and fund operations. This high‑autonomy role offers a rare opportunity to build a firm’s technical edge from the ground up.
Why this role is remarkable:
- Enjoy complete ownership of the firm’s data and AI strategy, building production‑ready systems that directly influence investment decisions and fund operations.
- Join a prestigious deep tech VC with $1bn AUM and a portfolio of 60+ companies across cutting‑edge sectors like quantum and semiconductors.
- Work in a high‑impact, low‑bureaucracy environment with direct access to partners, avoiding the siloed structures typical of larger financial institutions.
What You Will Do:
- Design and scale robust data foundations, including modeling, quality enrichment, and governance across all internal and external data sources.
- Build and deploy pragmatic AI/ML solutions, such as LLM‑enabled research and automated document analysis, to streamline sourcing and diligence.
- Partner directly with senior stakeholders to translate strategic investment needs into proprietary data products and scalable automated workflows.
The Ideal Candidate:
- Possesses 2–4 years of experience in data engineering or machine learning engineering with a proven track record of owning production‑level systems.
- Demonstrates technical mastery of Python, SQL, and APIs, with the ability to build end‑to‑end data solutions from ingestion to user‑facing outputs.
- Exhibits a commercial mindset and the ability to operate autonomously in ambiguous environments, translating complex business needs into technical leverage.
Product + Automation Engineer at IQ Capital in London employer: Jack & Jill
IQ Capital is an exceptional employer, offering a unique opportunity to work at the forefront of deep tech investment in London. With a lean team and a high-impact, low-bureaucracy environment, employees enjoy complete ownership of their projects, direct access to senior partners, and the chance to shape the future of technology through innovative data and AI solutions. The firm prioritises employee growth and fosters a collaborative culture that encourages autonomy and creativity, making it an ideal place for those seeking meaningful and rewarding careers.
StudySmarter Expert Advice🤫
We think this is how you could land Product + Automation Engineer at IQ Capital in London
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We think you need these skills to ace Product + Automation Engineer at IQ Capital 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!
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✨Brush Up on Your Statistics
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✨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.
