Data Analyst - Product and Growth
Studocu was founded on the principle that equal access to study notes reduces inequality between students. Today, we make it easy for more than 45+ million students to share notes every month. The product was a rocket ship from day one and has shown strong product-market fit on every continent, making us a key player in the exploding EdTech space. We received a $50 million Series B investment so we’re in rapid growth and scale mode, and we need serious talent to help us make it happen.
The whole team at Studocu is arranged around ten Product teams - cross-functional squads composed of Product, Tech, Design, Data, Marketing and Operations team members, working together on defining and building the future for a focus area of the business. We’re looking for a new Data Analyst to support the New Users, Discovery, and SEO pillars, who are responsible for user acquisition, engagement and retention.
You’ll report directly to our Director of Data, but work hand in hand with the pillar Product Managers to address some of the most challenging and exciting questions we’re facing as a company - defining and measuring user activity, engagement and retention, customer lifetime value and identifying their drivers; supporting marketing attribution; building highly performant page-linking algorithms; and designing, conducting and evaluating complex A/B/n tests.
One of our biggest challenges is that there are opportunities everywhere we look, so this role is ideal for someone very hands-on, who lives by the principles of lean development (“done is better than perfect”) and who knows how to validate assumptions with MVP solutions, and then scale iteratively as we learn what works.
What you’ll be doing here:
Owning product/growth analytics: help the product pillars to define and identify the drivers of user activation, engagement, retention, and customer lifetime value. Develop scalable page linking algorithms. Analyse our backlinking profiles, make comparisons with our competitors, and find opportunities for value improvements. Develop critical SEO KPIs (crawl depth, keyword rankings, page performance) and find opportunities for optimization.
Reporting & visualisation: support the squads and our data/analytics engineers to build the required data models and dashboards for proactive tracking and monitoring of KPIs
Building data-driven products: work with our data engineers and scientists to turn insights into production models, e.g. to improve user acquisition, activation and engagement strategies, to optimise advertising spending with CLV predictions, and to segment our user base for better personalisation.
Why you'll love it here:
Mandate to “break things” and “challenge status quo”.
Lots of data = lots of fun.
Data-driven, ambitious company that aims to be the market leader.
Ownership to build impactful data products with little bureaucracy.
We are an EdTech company, so we strongly value your personal learning & development.
Our recruitment process:
- Screening call with a recruiter.
- 1st interview with your future teammate.
- 2nd interview with your future manager.
- Assignment to do at home & assignment evaluation.
Relevant education (e.g. BSc in statistics, business analytics, quantitative marketing, data science, etc).
2+ years of experience in product analytics at online B2C companies.
Strong SQL knowledge (our main analytics language).
Proficient in working with a product analytics tool (we use Mixpanel).
Proficient in using at least one BI Tool (we use Metabase).
Solid experience designing and evaluating A/B/n tests.
Demonstrable understanding of digital marketing and user activation/retention.
Bonus points for experience with a scripting language (preferably Python).
Team player that is willing to collaborate with other departments.
Great communicator with the ability to present complex information into actionable insights.
Curious and resilient: you will often be out of your comfort zone since we are dealing with a broad range of challenges.
Autonomous: you can lead a data project from A to Z.
Lean: start with the simplest solution and scale it iteratively.
Good commercial instinct.
Something looks off?