Senior Data Scientist
Summary
Senior Data Scientist builds and deploys machine learning models for banking use cases like fraud detection and risk management, using Python, SQL, and cloud platforms.
Senior Data Scientist (Banking)
Cooperation model: B2B Contract
Industry: Banking
Start: July-August
About the project
We are looking for a Senior Data Scientist to join a long-term project for one of our clients from the banking sector. You will work on advanced analytics and AI initiatives that support business decision-making, risk management, fraud detection, and customer experience. As part of a multidisciplinary data team, you will transform complex data into production-ready machine learning solutions with real business impact.
Your responsibilities
Design, develop, and deploy machine learning models for banking use cases.
Analyze large, complex datasets to identify trends, patterns, and business opportunities.
Collaborate with Data Engineers, MLOps Engineers, Product Owners, and business stakeholders.
Build predictive models for areas such as fraud detection, credit risk, customer segmentation, and churn prediction.
Validate, monitor, and optimize model performance in production.
Prepare clear insights and recommendations for technical and non-technical stakeholders.
Contribute to the continuous improvement of the data science and MLOps ecosystem.
Must-have
5+ years of commercial experience as a Data Scientist.
Strong Python programming skills.
Hands-on experience with machine learning libraries such as scikit-learn, XGBoost, LightGBM, or CatBoost.
Experience with SQL and data analysis.
Solid understanding of statistics, probability, and predictive modeling.
Experience working with cloud platforms (AWS, Azure, or GCP).
Experience with Git and software development best practices.
Ability to communicate effectively with business stakeholders.
Fluent English (B2+).
Nice to have
Experience in the banking or financial services sector.
Knowledge of Databricks or Snowflake.
Experience with Spark or PySpark.
Hands-on experience with MLOps tools (MLflow, Kubeflow, Azure ML, SageMaker).
Familiarity with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), or Generative AI.
Experience with Docker and Kubernetes.
Knowledge of CI/CD pipelines for machine learning.
Tech stack
Python
SQL
scikit-learn
XGBoost / LightGBM
Pandas
NumPy
Spark / PySpark
Databricks (nice to have)
MLflow
Docker
Kubernetes
Azure / AWS / GCP
Git