At a Glance
- Tasks: Transform fragmented dashboards into a unified metric layer for the company.
- Company: Join Model ML, a $75M AI platform backed by Y Combinator.
- Benefits: Opportunity to build your own team and shape the company's analytics strategy.
- Other info: Dynamic work culture with potential for rapid career advancement.
- Why this job: Directly impact a growing startup and report to the CEO.
- Qualifications: Strong SQL and dbt skills with experience in high-growth environments.
The predicted salary is between 80000 - 100000 £ per year.
- Job Title
- Analytics Lead
- Salary
- Not Disclosed
- Company Description
Model ML is a $75M post-Series A, YC-backed AI platform building data tools for investment banks, private equity firms, and global consulting leaders.
Job Description
Reporting to the CEO, you will transform Model ML's analytics from fifty fragmented dashboards into a single, canonical metric layer.
This role requires technical depth in SQL and dbt combined with the business intuition to define core metrics for a scaling AI fintech, ensuring leadership runs on one source of truth.
- Location
- London, UK
- Why this role is remarkable
- Report directly to the CEO at a $75M post-Series A startup backed by Y Combinator, with founders who have exited two prior companies.
- Take full ownership of the company's metric strategy, presenting and defending core data definitions directly to leadership and potential future investors.
- Build your own team of one to two analysts within the first year as the company scales from 60 to 150 employees.
What You Will Do
- Consolidate a sprawling estate of 50+ dashboards into a defended core set of metrics that the entire company runs on daily.
- Embed self-serve analytics across non-technical functions, ensuring commercial and product teams can answer their own questions without relying on engineering.
- Maintain the semantic layer and core metric definitions, bridging the gap between raw warehouse data and high-level business strategy.
- The ideal candidate
- Deeply hands-on with SQL, dbt, and modern BI tools, with a track record of building production-grade data models and semantic layers.
- Proven experience navigating reporting sprawl at scale, ideally having consolidated complex data estates within high-growth or fintech environments.
- Thrives in a high-intensity \"9 to 9\" office culture and possesses the commercial acumen to translate business needs into technical specifications.
- #J-18808-Ljbffr
Analytics Lead at Model ML employer: Jack & Jill
At Jack & Jill, we pride ourselves on being an exceptional employer that fosters a dynamic and innovative work culture. Our team enjoys a range of benefits including flexible working arrangements, professional development opportunities, and a collaborative environment that encourages creativity and growth. Located in a vibrant area, we offer unique advantages such as access to cutting-edge technology and the chance to work with industry leaders in AI-driven marketing strategies.
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We think this is how you could land Analytics Lead at Model ML
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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 Jack & Jill.
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We think you need these skills to ace Analytics Lead at Model ML
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 Jack & Jill, 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 Jack & Jill. 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 Jack & Jill
✨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 Jack & Jill!
✨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.
