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 strategic teams - cross-functional squads composed of Product, Tech, Design, Data, Marketing and Operations, working together on defining and building the future for a focus area of the business. All these squads rely on data every day to make better decisions and improve the product experience for our users who are millions of students all around the globe. To support and accelerate the impact of each squad, we are building a data science function that will be identifying, prototyping, testing and implementing algorithmic solutions to key business and student challenges.

You’ll report directly to our Director of Data, and work closely with our data engineers and scientists, product managers, and content and operations specialists. The Data Team is growing fast in several functions (engineering, analytics, and science), so you will have a unique opportunity to contribute to shaping the culture and way of working of the team in general, but of course the data science function in particular.

What you’ll be doing here:

• Creating data science Proof Of Concepts (POC): together with product and content managers, you will identify and recast business and student challenges into algorithmic problems. You will build and iterate on POC solutions/models and help to design tests and define test KPIs to evaluate and improve these solutions.

• Identifying opportunities: understand student behaviour and needs to identify business opportunities and formulate solutions. You will be a key part of the (exploratory) analyses and the problem formulation process, as well as the development of (algorithmic) solutions.

• Developing: develop, test and drive the implementation of data science solutions in an E2E manner, using state-of-the-art methods and best practices in software engineering.

• Taking ownership: own the products that are developed or to be developed, together with the other data scientists to act as a subject matter expert and first contact on the data science products.)

Examples of projects you’ll work on:

• Develop new product features such as AI-driven question answering and advanced document search.

• Improve and maintain the existing document quality assessment model, supporting our operations team to identify study document quality and assess IP infringement risk.

• Build an AI Generated Content Detection Model.

• Fraud detection.

• Sales forecasting.

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.

• The data science team is still young, so you’ll have a unique opportunity to build up the foundations and best practices.

• You will have the autonomy to explore and innovate.

• We are an EdTech company, so we strongly value your personal learning & development.

Our recruitment process:

1. Screening call with a recruiter.
2. 1st interview with your future teammate and your future manager.
3. Assignment to do at home & assignment evaluation interview.
4. Final chat.

Hard skills:

• 2+ years of experience as a Data Scientist/Machine Learning Specialist.

• Proficiency in >= 1 scripting language, preferably Python (PySpark is a plus).

• Proficiency in SQL.

• Demonstrable experience with Natural Language Processing.

• Demonstrable experience in developing machine learning applications.

• Experience with VCS tools, preferably GitHub.

• Fluency in English (written and spoken).

• It would be great if you have experience with productionizing models in AWS (i.e. MLOps).

Soft skills:

• Team player that is willing to collaborate with other departments to identify the most promising use cases for data science solutions.

• Great communicator with the ability to present complex information to a non-technical audience.

• Autonomous: you take responsibility for individual data science products e2e.

• Lean: you have an MVP attitude and understand that adding 80% of value fast is preferred over adding 99% of value much later.