About
Must Have Technical/Functional Skills Primary: Artificial intelligence, ML, LLM Secondary: Data science, Python, NLP, Agile tools Experience: 10+
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.
Salary Range- $120,000-$135,000 a year
Languages
- English
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