Data Science Lead

TBC Bank Uzbekistan, the first digital bank in Uzbekistan and a part of TBC Bank Group PLC, commenced its operations in 2020. Today, the bank is rapidly developing, offering retail banking solutions through its innovative fintech platform. Over 4 million registered users have access to banking services via its mobile application. "Making People's Life Easier" - the core principle of TBC Bank's operations. By advancing digital technologies across all spheres of life, TBC Bank is dedicated to building a bank free from queues, physical offices, bureaucracy, complex terminologies, and lengthy waits.

We are looking for talented individuals with Data Science Lead  experience to join our Credit Risk Team.

Primary Focus: 

As the Data Science Lead in Credit Risk Team , you will be responsible for driving the design, development, and deployment of machine learning models to assess and mitigate credit risk. You will lead complex data science projects, collaborate with various stakeholders, and ensure that the solutions align with business objectives.

• Lead ML Projects End-to-End: Manage the entire lifecycle of machine learning projects, from initial business discussions through to model development, deployment, and ongoing monitoring.
• Collaborate with Stakeholders: Work closely with Data Engineers, Risk Managers, DevOps, MLOps, Data Architects, and other business stakeholders to ensure seamless model integration into production and alignment with organizational goals.
• Advanced Credit Risk Modeling: Design and develop sophisticated models using tree-based algorithms (e.g., XGBoost, LightGBM, CatBoost) and logistic regression to predict credit risk and assess loan defaults, fraud detection, or credit scoring.
• Model Optimization & Evaluation: Utilize best practices in hyperparameter tuning, cross-validation, and feature selection to create robust, high-performing models. Employ evaluation metrics such as AUC, ROC curves, Precision/Recall, and F1-Score to assess model performance.
• Data Engineering & ETL Pipeline Development: Work on building and optimizing ETL pipelines for data extraction, transformation, and loading. Leverage SQL, Pandas, and NumPy to process and analyze large datasets, ensuring that they are clean and ready for model development.
• Model Deployment & Monitoring: Implement MLOps and CI/CD practices to deploy machine learning models in production environments. Ensure models are monitored effectively for performance and accuracy, applying necessary updates as needed.
• Mentorship & Leadership: Lead a team of data scientists and mentor junior team members, fostering a culture of innovation, collaboration, and technical excellence.
• Business Insight Translation: Communicate complex technical findings and insights to non-technical stakeholders, ensuring that data science solutions meet business requirements and drive value for the bank.

Qualifications:

• Expertise in Machine Learning & Statistical Modeling:
• Proven experience with tree-based algorithms (e.g., XGBoost, LightGBM, CatBoost) and logistic regression.
• Strong background in model evaluation and hyperparameter tuning to improve performance and robustness.
• Deep knowledge of feature engineering, model selection, and model validation techniques.
  
  
• Proficiency in Python & Data Science Tools:
• Extensive experience with Python and data science libraries such as Pandas, NumPy, Scikit-learn, Seaborn, Matplotlib, and XGBoost.
• Expertise in building machine learning models and workflows, including working with pipelines, model training, and deployment.
  
  
• SQL & Data Manipulation:
• Advanced proficiency in SQL for querying large datasets, optimizing queries, and working with relational databases.
• Experience with data wrangling and transformation using Pandas and SQL for large-scale data processing.
  
  
• Big Data & Cloud Technologies:
• Familiarity with Hadoop, Spark, or Databricks for large-scale distributed data processing.
• Hands-on experience with cloud platforms such as AWS, GCP, or Azure, including data storage solutions (e.g., S3, BigQuery) and deployment of machine learning models.
• Proficiency with cloud-based MLOps tools and services for deploying and monitoring models at scale.
  
  
• ETL & Data Engineering Skills:
• Solid experience in ETL pipeline development, data integration, and working with data lakes or data warehouses.
• Knowledge of Apache Airflow, Kafka, or similar tools for orchestrating workflows and ensuring data consistency.
  
  
• Model Deployment & Monitoring:
• Experience deploying models into production environments using tools such as Docker, Kubernetes, and MLflow.
• Familiarity with model performance monitoring and tracking, utilizing tools to monitor model drift and take corrective actions as necessary.
  
  
• Collaboration & Communication:
• Strong communication skills with the ability to translate complex technical concepts to business stakeholders.
• Experience in leading cross-functional teams, collaborating with data engineers, MLOps, and other business units to ensure model deployment success.

Desired Experience & Skills:

• Credit Risk Management: Direct experience in credit risk modeling, loan default prediction, fraud detection, or credit scoring models in a financial institution or similar environment.
• Cloud & Big Data Expertise: Hands-on experience with AWS SageMaker, GCP BigQuery, or Azure ML for building, training, and deploying machine learning models.
• Advanced Techniques: Exposure to deep learning (e.g., TensorFlow, Keras) and natural language processing (NLP) for advanced credit risk modeling.
• Risk Management Knowledge: Familiarity with financial regulations like Basel III, CCAR, or stress testing that influence risk modeling approaches.
• Languages:  English - B1/B2;  Russian - B1/B2

Working with us means:

• An open atmosphere and vibrant team life without unnecessary bureaucracy or dress code;
• Medical insurance with extended coverage after the probation period;
• Coverage for conference participation, discounts for English language courses, and access to educational platforms (Coursera, Udemy);
• Internal meetups: lightning & thunder talks;
• High-performance equipment and additional monitors for productive work;
• Class “A” office in the city center
• A multicultural team (Georgia, Russia, Poland, Kazakhstan, Kyrgyzstan, Ukraine, Tajikistan);
• Referral program "Bring a Friend" with bonus opportunities;
• Coffee points for snacks at any time;
• Team-building and sports events that foster team spirit.