At a Glance
- Tasks: Own data integrity and investigate anomalies in financial datasets.
- Company: Join a leading quantitative finance firm with a focus on innovation.
- Benefits: Competitive salary, annual bonus, 35 days leave, and comprehensive healthcare.
- Other info: Enjoy a flexible work environment with monthly company events.
- Why this job: Shape impactful research tools and collaborate with top-tier engineers.
- Qualifications: Strong SQL and Python skills, plus a collaborative mindset.
The predicted salary is between 60000 - 75000 £ per year.
We tackle the most complex problems in quantitative finance, by bringing scientific clarity to financial complexity. From our London HQ, we unite world-class researchers and engineers in an environment that values deep exploration and methodical execution - because the best ideas take time to evolve. Together we're building a world-class platform to amplify our teams' most powerful ideas.
As part of our engineering team, you'll shape the platforms and tools that drive high-impact research - designing systems that scale, accelerate discovery and support innovation across the firm.
Role Overview: Reference Data Data AnalystThe role Reference Data underpins G-Research's trading and research workflows by curating core market datasets. As a Data Analyst, you will own day-to-day data integrity and investigate anomalies end to end. You will partner with engineers and stakeholders to improve pipelines, controls and downstream usability. This is a highly stakeholder-facing role requiring proactive communication, accountability and strong teamwork.
Key Responsibilities- Owning daily data quality across reference data products including instruments, corporate actions, calendars, pricing and identifiers
- Designing and maintaining data quality checks, dashboards and alerting
- Leading root cause analysis on incidents including trace lineage, identifying failure modes and driving remediation with engineering
- Communicating proactively with stakeholders, setting expectations and explaining impact, severity and timelines
- Driving issues to closure through clear ownership, tracking actions, validating fixes and preventing regressions
- Contributing as a strong team player by documenting learnings, sharing context and improving team standards
The ideal candidate will have the following skills and experience:
- Strong SQL skills
- Strong Python skills for dataframe analytics using pandas or polars
- Confident use of AI coding assistants with sound judgment and validation
- Financial markets domain knowledge
- A collaborative approach to working with stakeholders
- An automation-first mindset to reduce RTB and eliminate recurring manual work
- Familiarity with Git, notebooks and reproducible analytics practices
- Experience working with engineers in modern deployment environments such as containers or Kubernetes
- Highly competitive compensation plus annual discretionary bonus
- Lunch provided (via Just Eat for Business) and dedicated barista bar
- 35 days' annual leave
- 9% company pension contributions
- Informal dress code and excellent work/life balance
- Comprehensive healthcare and life assurance
- Cycle-to-work scheme
- Monthly company events
G-Research is committed to cultivating and preserving an inclusive work environment. We are an ideas-driven business and we place great value on diversity of experience and opinions. We want to ensure that applicants receive a recruitment experience that enables them to perform at their best. If you have a disability or special need that requires accommodation please let us know in the relevant section.
Data Analyst employer: Aplaro Ltd
Baringa is an exceptional employer that fosters a collaborative and innovative work culture, particularly in the vibrant Energy & Infrastructure sector. With a strong focus on employee development, you will have access to numerous growth opportunities while working alongside industry experts on impactful projects that shape the future of energy and utilities. Located in a dynamic environment, Baringa offers a unique chance to engage with diverse clients and contribute to meaningful change in the renewable infrastructure landscape.
StudySmarter Expert Advice🤫
We think this is how you could land Data Analyst
✨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 Aplaro Ltd!
✨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 Data Analyst at Aplaro Ltd.
✨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 Aplaro Ltd.
✨Apply Directly through Our Website
When you find a suitable opening like Data Analyst at Aplaro Ltd, 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 Data Analyst
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 Aplaro Ltd, 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 Aplaro Ltd. 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 Aplaro Ltd
✨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 Aplaro Ltd!
✨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.
