Data Scientist

Roles & Responsibilities

Key Responsibilities
▪ Develop and deploy machine learning models for analytics and automation.
▪ Analyze complex datasets to support business strategy and decision-making.
▪ Design scalable solutions to improve financial service operations.
▪ Maintain data pipelines and enable real-time analytics capabilities.
▪ Enhance risk assessment and fraud detection frameworks using ML techniques.
▪ Communicate insights effectively through dashboards and reports.
Key Technical Requirements
▪ Strong Python programming with clean coding best practices.
▪ Experience with Flask, FastAPI, or Django frameworks.
▪ Hands-on experience with containerisation and orchestration tools.
▪ Experience in LLM fine-tuning, prompt engineering, and agentic frameworks (LangChain, LangGraph).
▪ Data processing expertise using Pandas, NumPy, SQL, and ideally PySpark.
▪ Cloud exposure, preferably Azure and Databricks.
▪ Experience with CI/CD pipelines and GenAI/LLM production deployment.
Knowledge & Experience
▪ Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related field.
▪ 8–10 years of overall IT experience including data science, ML, AI engineering, or software engineering.
▪ Experience handling large datasets and distributed computing (Spark or similar).
▪ Ability to translate business problems into scalable production-ready solutions.
▪ Strong communication and stakeholder collaboration skills.
Additional Qualifications
▪ Proficiency in Python, R, or Scala.
▪ Experience with TensorFlow, PyTorch, and Scikit-learn.
▪ Strong knowledge of statistics, probability, and optimization techniques.
▪ Familiarity with AWS, GCP, or Azure.
▪ Financial services domain experience will be an added advantage.