Data scientist

Roles & Responsibilities

• Experience with NLP, deep learning, or time series analysis.
• Experience deploying models to production environments.
• Knowledge of regulatory requirements and compliance in banking and finance.
• Familiarity with MLOps practices and tools.
• Experience with Agile methodology and tools (JIRA or Rally)
• Proven experience as a Data Scientist in Banking or a similar domain
• Proficiency in Python or R, and experience with data science libraries (e.g., pandas, scikit-learn, TensorFlow, PyTorch).
• Hands-on experience with large language models (e.g., OpenAI GPT, Llama, or similar), including fine-tuning and prompt engineering.
• Strong knowledge of statistics, machine learning, and data mining techniques.
• Experience with data visualization tools (e.g., Tableau, Power BI).
• Experience with Big Data Platforms (Hadoop).
• Familiarity with SQL and working with relational databases.
• Excellent problem-solving, communication, and collaboration skills.
• Experience with cloud platforms (AWS, Azure, or GCP) is a plus.
• Analyze large financial datasets to extract insights and support business decisions.
• Develop, implement, and evaluate machine learning models and algorithms tailored to banking and finance use cases (e.g., risk modeling, fraud detection, customer segmentation).
• Apply and fine-tune large language models (LLMs) for tasks such as document analysis, customer communication, and regulatory compliance.
• Collaborate with cross-functional teams to understand business requirements and deliver data-driven solutions.
• Communicate findings and recommendations through reports, dashboards, and presentations.
• Work with data engineers to ensure data quality and pipeline reliability.