Quantitative Portfolio Architect & Analytics Engineer in London

Quantitative Portfolio Architect & Analytics Engineer in London

London Full-Time 60750 - 74250 £ / year (est.) No working from home possible
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At a Glance

  • Tasks: Research and develop innovative portfolio solutions using advanced statistics and machine learning.
  • Company: Join Invesco, a leading global investment firm with a focus on innovation.
  • Benefits: Competitive salary, creative opportunities, and a dynamic work environment.
  • Other info: Exciting career growth within a collaborative global team.
  • Why this job: Make a real impact in the investment world with cutting-edge technology.
  • Qualifications: Strong background in quantitative analysis and data management.

The predicted salary is between 60750 - 74250 £ per year.

Invesco is seeking a senior quantitative professional to join the Portfolio Construction and Engineering team in London. You will help evolve investment capabilities by researching, managing data, and developing APIs powering the investment process and Invesco Vision®. The role offers substantial creative and innovative opportunities within a global platform. You will apply advanced statistics, optimization, and ML techniques to build robust portfolio solutions and support risk and performance.

Quantitative Portfolio Architect & Analytics Engineer in London employer: Invesco

Invesco is an exceptional employer that fosters a collaborative and inclusive work culture in the picturesque setting of Henley-on-Thames. Employees benefit from comprehensive professional development opportunities, competitive compensation packages, and a commitment to work-life balance, making it an ideal place for those looking to grow their careers in AML and compliance within the financial services sector.

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Contact Details:

Invesco Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Quantitative Portfolio Architect & Analytics Engineer in London

Get Involved in Data Science Meetups

Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Invesco!

Show Off Your Projects

Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Quantitative Portfolio Architect & Analytics Engineer at Invesco.

Leverage Professional Networks

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 Invesco.

Apply Directly through Our Website

When you find a suitable opening like Quantitative Portfolio Architect & Analytics Engineer at Invesco, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!

We think you need these skills to ace Quantitative Portfolio Architect & Analytics Engineer in London

Advanced Statistics
Data Management
API Development
Machine Learning Techniques
Portfolio Construction
Risk Analysis
Performance Analysis

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 Invesco, 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 Invesco. 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 Invesco

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 Invesco!

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.