Data Science Analyst

Responsibilities:

• Analyze large structured and unstructured datasets to identify trends, patterns, and business insights.
• Perform data cleansing, transformation, and feature engineering to support model development.
• Develop, test, and maintain predictive and prescriptive models using statistical and machine learning techniques.
• Support the deployment of analytical solutions into production environments in partnership with technology teams.
• Contribute to the implementation of machine learning lifecycle processes, including development, testing, training, monitoring, and performance evaluation.
• Collaborate with business, technology, and risk partners to understand requirements and translate them into analytical solutions.
• Document methodologies, assumptions, and model results to support governance and review processes.
• Present analytical findings and project updates to team members and stakeholders.
• Continuously learn and apply emerging techniques in Machine Learning, Deep Learning, Large Language Models (LLMs), and Generative AI.

Requirements:

• Master's degree or Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field.
• Minimum 5 years of experience in data science, machine learning, advanced analytics, or a related field.
• Experience developing and evaluating machine learning models.
• Working knowledge of ML/DL techniques and model development processes.
• Proficiency in Python, SQL, Spark, PySpark, TensorFlow, or similar analytical and model-building tools.
• Familiarity with LLMs and GenAI technologies.
• Strong analytical, problem-solving, and communication skills.
• Ability to work independently while collaborating effectively within cross-functional teams.

Preferred Skills:

• Experience supporting ML, AI, or GenAI initiatives in a production environment.
• Familiarity with distributed data and computing platforms such as Hadoop, Hive, Spark, or cloud-based analytics platforms.
• Exposure to banking, Retail Risk management, or financial services.
• Basic understanding of capital markets, financial instruments, and quantitative modeling concepts.

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