Senior Data Analytics & AI Assurance Lead in London

Senior Data Analytics & AI Assurance Lead in London

London Full-Time 70000 - 90000 £ / year (est.) No working from home possible
hackajob

At a Glance

  • Tasks: Lead AI and data risk transformation programmes while building strong client relationships.
  • Company: Join EY, a leader in financial services and innovation.
  • Benefits: Competitive salary, professional development, and opportunities for career advancement.
  • Other info: Dynamic role with a focus on innovation and growth.
  • Why this job: Shape the future of analytics and drive impactful change in the industry.
  • Qualifications: Experience in risk, controls, audit, and proficiency in Python and SQL.

The predicted salary is between 70000 - 90000 £ per year.

hackajob is collaborating with EY to find a Senior Manager - Data Analytics in London. This role involves leading AI and data-enabled risk transformation programmes and nurturing senior client relationships in Financial Services.

The ideal candidate will have significant experience in risk, controls, and audit, with expertise in technologies like Python and SQL.

This position offers a chance to influence the growth of analytics capabilities within the firm, promoting innovation and effective practices.

Senior Data Analytics & AI Assurance Lead in London employer: hackajob

EY is an exceptional employer that fosters a culture of innovation and collaboration, particularly in the vibrant city of London. With a strong focus on employee growth, EY offers extensive training and development opportunities, enabling you to enhance your skills in data analytics and AI while working alongside industry leaders. The firm values diversity and inclusion, creating a supportive environment where your contributions can truly make an impact in the Financial Services sector.

hackajob

Contact Details:

hackajob Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Senior Data Analytics & AI Assurance Lead 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 hackajob!

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 Senior Data Analytics & AI Assurance Lead at hackajob.

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 hackajob.

Apply Directly through Our Website

When you find a suitable opening like Senior Data Analytics & AI Assurance Lead at hackajob, 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 Senior Data Analytics & AI Assurance Lead in London

Data Analytics
AI Assurance
Risk Management
Controls and Audit
Client Relationship Management
Python
SQL

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 hackajob, 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 hackajob. 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 hackajob

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 hackajob!

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.