Sr. Data Scientist Engineer

About the role

• Research, develop, and deploy Graph Neural Network (GNN) models, GraphRAG pipelines, and LLM-based solutions for real-world applications.
• Design and implement fraud detection models leveraging machine learning, NLP, and graph-based techniques.
• Build and optimize interactive content discovery systems using Vector Databases and embedding-based techniques.
• Develop and maintain end-to-end ML pipelines from data ingestion to model deployment and monitoring.
• Work with AWS cloud services (e.g., SageMaker, Databricks, Lambda, S3, Kinesis) to scale AI/ML workflows.
• Optimize models for efficiency, performance, storage, and cost, ensuring they drive measurable business impact (e.g., content discovery, fraud prevention).
• Collaborate with data engineering and software teams to ensure smooth deployment, monitoring, and scalability of AI/ML models in production.
• Communicate technical concepts effectively to both technical and non-technical stakeholders.

About You

• Bachelor s or Master s degree in Data Science, Computer Science, Statistics, or a related field.
• 8+ years of experience in data science, with a strong focus on machine learning, AI, and production-grade model deployment.
• Strong expertise in AI/ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
• Hands-on experience with Graph Neural Networks, GraphRAG, LLMs, Graph and Vector Databases.
• Experience with fraud analysis using machine learning, graph analytics, or NLP techniques.
• Proficiency in Python, R, or similar programming languages.
• Strong knowledge of cloud platforms (AWS preferred) and experience with data-related services (e.g., Databricks, SageMaker, Kinesis, Lambda).
• Ability to optimize AI/ML models for performance, efficiency, and cost-effectiveness.
• Strong analytical, problem-solving, and communication skills, with the ability to explain complex ML concepts to cross-functional stakeholders.