Growth Analyst β€” FinTech Data & Growth (Hybrid)

Growth Analyst β€” FinTech Data & Growth (Hybrid)

Full-Time 58500 - 71500 Β£ / year (est.) Home office (partial)
Lendable

At a Glance

  • Tasks: Lead marketing and growth analytics for a multi-product app using data-driven insights.
  • Company: Join Lendable, a dynamic FinTech company focused on innovation.
  • Benefits: Enjoy a hybrid work model, competitive salary, and opportunities for professional growth.
  • Other info: Collaborate with diverse teams in a fast-paced, innovative environment.
  • Why this job: Make an impact by optimising user acquisition and engagement through data analysis.
  • Qualifications: Strong analytical skills and experience in marketing or growth analytics.

The predicted salary is between 58500 - 71500 Β£ per year.

Lendable is seeking a Growth Analyst to lead marketing and growth analytics across a multi-product app.

You will drive data-informed decisions to optimise acquisition, engagement and product features using LTV models, experimentation, and robust reporting.

You will collaborate with product, growth and engineering teams to ensure decisions are grounded in reliable data, shaping the strategy across channels and campaigns.

#J-18808-Ljbffr

Growth Analyst β€” FinTech Data & Growth (Hybrid) employer: Lendable

Lendable is an exceptional employer that champions innovation and flexibility, making it an ideal place for a Senior Kotlin/JVM Engineer to thrive. With a vibrant work culture that prioritises collaboration and personal growth, employees are encouraged to develop their skills while contributing to impactful financial products. The company's commitment to flexible working arrangements further enhances the work-life balance, making it a rewarding environment for those looking to make a difference in the fintech space.

Lendable

Contact Details:

Lendable Recruitment Team

StudySmarter Expert Advice🀫

We think this is how you could land Growth Analyst β€” FinTech Data & Growth (Hybrid)

✨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 Lendable!

✨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 Growth Analyst β€” FinTech Data & Growth (Hybrid) at Lendable.

✨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 Lendable.

✨Apply Directly through Our Website

When you find a suitable opening like Growth Analyst β€” FinTech Data & Growth (Hybrid) at Lendable, 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 Growth Analyst β€” FinTech Data & Growth (Hybrid)

Data Analysis
Marketing Analytics
Growth Strategy
LTV Models
Experimentation
Reporting
Collaboration

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 Lendable, 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 Lendable. 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 Lendable

✨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 Lendable!

✨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.