Lendo AB

Data Scientist

Lendo AB
SE Stockholm, Stockholms län
Onsite 2026-06-24
Estimated salary · Stockholm
~ SEK 537,600 - SEK 730,800
Low
SEK 537K
Median
SEK 620K
High
SEK 730K
Market in Stockholm · SCB 2025
Estimated net pay
SEK 35,081 - SEK 44,836
/month · 22% withheld
after tax & contributions · on the estimated salary · Individual taxation — marital status and dependents do not affect it

Job description

At Lendo, we’re driven by a clear mission: to empower people to make smarter financial decisions. Our work directly impacts how users understand and navigate complex financial journeys, and we take that responsibility seriously. We build our success on a culture where we live by our core values: customer centric, win as a team, focus on execution, and always improve. We are convinced that it is diverse perspectives, differences, and a strong collective drive that allow us to continue evolving. We are always looking for colleagues who wants to grow together with us. About the role We are looking for a Data Scientist to drive business impact through machine learning. We have already created significant value from ML across several areas and see strong opportunities to apply it even more broadly, including marketing, conversion optimization, and operational decision-making. We are looking for a Data Scientist who has successfully created business impact with ML and is energized by delivering measurable results. What you will do Build, deploy, and improve machine learning models that drive measurable impact across domains such as marketing, conversion optimization, and operational efficiency. Design, analyze, and interpret experiments, causal analyses, and marketing mix models (MMM) to guide marketing investments. Be a trusted advisor within ML and partner with product, business, and engineering teams to translate problems into ML solutions. Ensure models deliver continuous business value. About you 3+ years of experience as a Data Scientist, with a track record of shopping models that created measurable business value. Strong modeling expertise with solid foundations in statistics and machine learning, and a clear understanding of when and how to apply different methods Experience designing and interpreting experiments and casual analyses Ability to work effectively with non-technical stakeholders.. Ability to take initiative and drive ML opportunities

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