Job Description

Job Purpose

We are seeking a talented and motivated Data Scientist for a temporary position to support our growing data and automation needs. This role focuses on building robust, Python-based solutions to streamline data processes, enhance system integration, and enable intelligent automation across the organization. You will work closely with internal teams to develop and maintain automation scripts, manage data pipelines, and support the integration of AI capabilities.

The ideal candidate is passionate about data engineering, comfortable navigating complex systems, and excited by the opportunity to operationalize AI within a modern enterprise environment.

Responsibilities

• Design, develop, and maintain scalable Python-based data pipelines for processing large volumes of structured and unstructured data.
• Build automation scripts to streamline recurring data workflows, monitoring tasks, and data quality validation processes.
• Collaborate with data scientists to support AI and machine learning model deployment, feature engineering, and production data flow integration.
• Write efficient and maintainable SQL queries to support reporting, analytics, and data exploration needs.
• Participate in the development of internal tools to improve data access and usability across the organization.
• Monitor and improve data pipeline reliability, including logging, alerting, and performance tuning.
  
  

Knowledge And Experience

• 3+ years of experience as a Data Scientist or in a similar role.
• Strong programming skills in Python, with experience writing reusable libraries and working with data manipulation libraries
• Advanced proficiency in SQL and experience working with large-scale databases (e.g., PostgreSQL, MSSQL, Oracle…).
• Experience with AI/ML workflows, supporting model training and inference pipelines in production environments.
• Solid understanding of data modeling, warehousing, and distributed data processing concepts.
  
  

Advantage

• Exposure to real-time data processing technologies (e.g., Kafka, Spark Streaming).
• Background in finance, trading systems, or financial data pipelines.