Data Scientist

Summary

Join a leading consulting firm as an experienced Data Scientist. This role centers on ensuring high-quality data preprocessing, model performance monitoring, and efficient collaboration with technical teams for successful deployments. You will be responsible for data processing, model evaluation, and deployment, working with large-scale data pipelines and cloud platforms. The ideal candidate possesses strong data science skills, experience with blob storage, and expertise in model review and debugging. Collaboration with cross-functional teams is crucial for success in this position. This is a hands-on role requiring proficiency in data preprocessing, cleansing, and feature engineering.

Requirements

• 5+ years of experience as a Data Scientist or in a similar role
• Strong proficiency in data preprocessing, data cleansing, and feature engineering
• Experience with blob storage and working with large-scale data pipelines
• Expertise in machine learning model review, debugging, and performance tuning
• Familiarity with DevOps principles, CI/CD pipelines, and environment setup for data science projects
• Hands-on experience with cloud platforms (e.g., Azure, AWS, or GCP) and related services
• Strong programming skills in Python, R, or Scala, with experience in relevant data science libraries
• Ability to monitor and troubleshoot model performance post-deployment, ensuring continuous optimization
• Excellent problem-solving skills and the ability to collaborate with cross-functional teams

Responsibilities

• Read input data from the blob storage, process it, and write the output back to the blob
• Perform data preprocessing and cleansing to ensure data integrity and accuracy
• Conduct model and code reviews, applying necessary changes if failures occur
• Collaborate with Technology & Infrastructure (T&I) and DevOps teams to streamline model deployment and ensure system reliability
• Assist in the creation of new environments for data science workflows
• Develop and maintain pipeline assets to support model deployment and monitoring
• Continuously monitor model performance post-deployment, identifying anomalies and optimizing models as needed