Building Scalable Data Solutions

13 hours ago

Islamabad, Islamabad, Pakistan beBeeDataScientist Full time $90,000 - $120,000
Our team is seeking a seasoned Senior Data Scientist to join our ranks. As a key member of our Data and AI Engineering Team, you will play a vital role in creating innovative and scalable data solutions. Our team specializes in cloud technologies, particularly AWS and Snowflake, with a focus on data security and governance. We prioritize collaboration, working closely with Data Scientists and ML Engineers to drive impactful data projects forward.In this role, you will design and implement scalable data architectures that align with strategic goals. You will leverage your expertise in Snowflake, Python, and cloud technologies to maintain strong data security and integrity. Your work will be instrumental in developing, optimizing, and maintaining production-level data pipelines that support both analytical and predictive modeling. This will ultimately enhance data processing efficiency and enable data-driven decision-making across the organization. ML Data Pipeline Development:
  1. Create and maintain scalable, reliable, and secure data infrastructure essential for Machine Learning model training, validation, and inference in production settings.
Feature Store Collaboration:
  1. Partner with Data Scientists to design, build, and maintain a centralized Feature Store, ensuring consistency, reusability, and low-latency access to features for model training and deployment.
GenAI Data Handling:
  1. Develop and execute data ingestion and processing workflows for text and unstructured data, including methods for vectorisation and populating vector databases to support Retrieval-Augmented Generation (RAG) architectures.
Core Data Engineering:
  1. Build and maintain robust ELT/ETL processes within Snowflake and AWS to integrate diverse, large-scale data sources, ensuring excellent data governance, quality, and performance for general business intelligence.
MLOps Partnership:
  1. Collaborate closely with ML Engineers to operationalise data flows to and from MLOps platforms (e.g., SageMaker, MLflow), focusing on automating pipelines and managing data versioning.
Query and Compute Efficiency:
  1. Optimize data storage and computing resources across the platform to speed up data science experiments and model training workloads, while managing costs effectively.
Data Security and Compliance:
  1. Implement advanced security measures to safeguard sensitive data used in AI datasets, maintaining data integrity and confidentiality in line with governance standards.

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