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About the Role

As a Decision Scientist at Vinted, you will leverage data to drive impactful decision-making, shaping product and business strategy to empower members towards more sustainable lives. You will join the Marketing Modelling Intelligence team to guide high-stakes decisions across channels and markets, maintain and improve Bayesian Media Mix Models, develop ROI and payback measurement, and apply statistical and econometric models to guide data-driven investment decisions.

Responsibilities

Co-create the development of our Bayesian Media Mix Models (MMM) and partner with experts to evolve our global ROI and payback frameworks.
Act as a strategic advisor to Marketing leaders, using econometric insights and statistical modelling to guide high-stakes investments across channels and markets.
Identify growth opportunities and answer critical business questions by designing rigorous tests and analysing impact using advanced statistical methods.
Design and deploy automated data products and self-service tools that empower the domain to make data-driven decisions independently and at scale.
Oversee key performance indicators and promote analytics best practices, ensuring our data culture translates directly into business results.

Requirements

Solid foundational experience (2–4 years) in data science, analytics, or a similar field.
Experience in marketing analytics and measurement is a great benefit, but not a must.
A strategic mindset: connect scientific findings to pragmatic business solutions that drive real-world growth.
Good communicator: skilled at influencing stakeholders and can present complex modelling results in a persuasive way to both technical and non-technical audiences.
Proficient in Python and SQL.
Hands-on experience using dbt and BI tools (like Looker or Tableau) to build scalable data products.
Deep knowledge of principles behind data science, including statistical modelling and regression analysis.
Experience with Bayesian statistics is a significant plus.
Excellent written and spoken English.
Previous experience with Media Mix Modelling (MMM) or advanced attribution methods will be considered as advantage.

Skills

Python
SQL
dbt
Looker
Tableau
Statistical modelling
Regression analysis
Bayesian statistics
Media Mix Modelling (MMM)
Advanced attribution methods

Location

Vilnius

Work Type

Hybrid Work

Experience Level

2-4 years

Education Level

Graduate degree in Data Science, Statistics, Math, Economics, Econometrics or a related quantitative field.

Salary/Compensations

€61.200—€82.800 EUR

Benefits

Share options programme
30 days of paid annual leave
Newest MacBook models
Digital mental and emotional health support and Employee Assistant Program (EAP)
Home office support: IT workstation equipment and a personal budget of up to €540 for home workplace furniture
Lunch benefit per your workday
Frequent team-building events
A personal monthly budget for shopping on Vinted
Access to a discounted gym membership plan
Pension Plan with Vinted matching 150% of your chosen contribution
Supplemental Private Health Insurance
Life and Disability insurance
A subsidised Deutschlandticket for your commute to the office by public transport
The opportunity to spend up to 90 days per year - 21 of which can be spent working outside of the EU - on workation
A dog-friendly office
Individual Learning Budget: annual learning budget to support personal and career development through courses, certifications, workshops and more.

About the Company

Vinted's mission is to make second-hand the first choice.
The Vinted Group is made up of Vinted Marketplace (Europe’s leading platform for second-hand fashion), Vinted Go (enhancing the shipping experience), and Vinted Pay (dedicated to bringing secure, reliable payments).
Founded in 2008 in Lithuania, Vinted became Lithuania's first unicorn in 2019.
Headquarters remain in Vilnius, with offices across Europe and a team of over 2,000 people.

Equal Opportunity

The Vinted Group is committed to building an inclusive workplace where people from all walks of life feel a sense of belonging.
All applicants are treated fairly without regard to their race, age, religion or belief, sex, national origin, citizenship, gender identity, sexual orientation, disability, or any other protected characteristic.