Multitude

Data Scientist

Lithuania · Work style not stated · Full time

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About the role

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EmployerMultitude
LocationLithuania
Work styleWork style not stated
Employment typeFull time
Employer posted15 Sep 2026

Role Overview

We are looking for a Data Scientist to build and deploy end-to-end machine learning solutions that power key fintech use cases such as credit risk modeling, customer lifecycle optimization, fraud detection, and collections strategies.

This is a role where your work directly influences key business decisions across risk, growth, and collections. As a Data Scientist, you’ll operate at the intersection of data, product, and engineering—developing scalable, production-level machine learning solutions in a modern Azure ML environment. You will take ownership of the full ML lifecycle, from problem definition to deployment and monitoring, while collaborating closely with stakeholders to turn data into insights that drive real, measurable impact.

Key Responsibilities

Own the full lifecycle of machine learning solutions: problem framing, data exploration, feature engineering, model development, deployment, and monitoring

Show full description

Develop and productionize models for credit scoring, marketing optimization, collections, and other core business problems

Work with large-scale data to build robust data pipelines and feature datasets

Deploy and manage models usingAzure Machine Learning, including experiment tracking, model versioning, and lifecycle management

Collaborate closely with stakeholders (product, risk, marketing, engineering) to identify high-impact opportunities and translate them into data solutions

Design, implement, and analyze A/B tests and experiments, ensuring statistically sound and business-relevant conclusions

Build monitoring frameworks to track model performance, detect data/model drift, and ensure long-term reliability

Ensure models meetregulatory and explainability requirements (e.g., credit decision transparency)

Communicate insights and model behavior clearly to both technical and non-technical stakeholders

Requirements

Degree in Data Science, Statistics, Mathematics, Econometrics, or a related field

Strong programming skills inPython and SQL (R is a plus)

Solid understanding of machine learning techniques and their practical trade-offs

Experience withAzure Machine Learning or similar platforms (AWS SageMaker, GCP Vertex AI)

Experience deploying models into production and maintaining them (monitoring, retraining, versioning)

Strong knowledge of experimentation and statistical methods (A/B testing, hypothesis testing)

Experience withmodel explainability techniques (e.g., SHAP, LIME), especially in regulated environments

Ability to translate complex analyses into clear business insights

Fluent in English

Nice to Have

Experience in fintech domains such ascredit risk, fraud detection, or collections optimization

Experience working withdistributed data processing frameworks (e.g., Apache Spark, Databricks)

Familiarity withMLOps practices (CI/CD, model registries, pipeline orchestration)

Experience withfeature stores and production data pipelines

Experience working inregulated environments (e.g., GDPR, model validation standards)

The salary range is €4,000 to €5,500 gross monthly. Our final offer to you will be set up fairly, considering your skills and experience.

We offer:

A Truly Global Workplace – work with professionals from 40+ nationalities, bringing diverse expertise, perspectives, and a collaborative international culture.

Hybrid & Flexible Work – we support work-life balance with remote work options and modern office spaces across Europe.

A Culture of Growth – we invest in your future, offering LinkedIn Learning, mentorship, and professional development programmes, including HiPo and leadership development initiatives to support career advancement.

Financial Growth Opportunities – benefit from our share purchase matching programme, allowing you to invest in your future with matched contributions and long-term financial rewards.

Workation Programme – work remotely from different countries for up to 2 months per year, experiencing new cultures while staying connected and productive.

We may use artificial intelligence (AI) tools to support specific parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses against predefined criteria. These tools assist our recruitment team but do not replace human judgment. All final hiring decisions are made by human recruiters.

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