Senior Data Scientist

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