Senior Data Scientist
⚲ Warszawa, Wrocław
19 000 - 25 000 PLN netto (B2B)
Wymagania
- Python
- NumPy
- Spark
- Docker
- Kubernetes
Opis stanowiska
Senior Data Scientist (Banking)
Cooperation model: B2B Contract
Industry: Banking
Start: July-AugustAbout 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
Cooperation model: B2B Contract
Industry: Banking
Start: July-AugustAbout 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
🔍 Dekoder Ogłoszenia
🔴
transform complex data into production-ready machine learning solutions with real business impact
Oczekuje się, że będziesz nie tylko tworzyć modele, ale także wdrażać je do produkcji i udowadniać ich wartość biznesową.
🟡
long-term project
Projekt może trwać długo, ale nie gwarantuje to stabilności zatrudnienia ani rozwoju w ramach tego projektu.
🟡
multidisciplinary data team
Możesz pracować z różnymi specjalistami, ale może to też oznaczać, że będziesz musiał wypełniać luki w kompetencjach innych członków zespołu.
🔴
production-ready machine learning solutions
Oprócz tworzenia modeli, będziesz odpowiedzialny za ich wdrożenie i utrzymanie w środowisku produkcyjnym, co może wymagać dodatkowych umiejętności MLOps.
🟢
clear insights and recommendations for technical and non-technical stakeholders
Będziesz musiał umieć komunikować złożone zagadnienia techniczne w sposób zrozumiały dla osób bez wiedzy technicznej.