Data Scientist
<p><b>About DXC Technology</b> </p><p>DXC Technology (NYSE: DXC) is a leading global provider of information technology services. We’re a trusted operating partner to many of the world’s most innovative organizations, building solutions that move industries and companies forward. Our engineering, consulting and technology experts help clients simplify, optimize and modernize their systems and processes, manage their most critical workloads, integrate AI-powered intelligence into their operations, and put security and trust at the forefront. Learn more on dxc.com.</p><p>Job Description:<br> </p><p><b>Data Science Senior Engineer</b></p><p><b>Roles and Responsibilities</b></p><ul><li>Lead end-to-end ML/AI solution architecture on AWS, from data ingestion and feature engineering to model training, fine-tuning, evaluation, deployment, monitoring, and retraining.</li><li>Design and implement production-grade ML pipelines using Amazon SageMaker AI (Pipelines, Processing, Training, HyperPod for distributed training, JumpStart, Canvas for no-code/low-code, Unified Studio integration).</li><li>Customize and deploy generative AI applications with Amazon Bedrock: select/invoke foundation models (e.g., Anthropic Claude, Amazon Nova, Meta, etc.), build agents (AgentCore, multi-agent orchestration), implement RAG via Knowledge Bases, apply Guardrails for safety/compliance, and optimize inference (Provisioned Throughput, cross-region inference).</li><li>Build scalable data processing and ETL pipelines integrating AWS Glue, EMR (for Spark-based big data ML), Athena (serverless querying), Lake Formation (governance), and S3/Data Lake for feature stores and datasets.</li><li>Develop MLOps practices: automate CI/CD for models (SageMaker Projects, AWS CodePipeline, Git), implement model monitoring (SageMaker Model Monitor, drift detection), A/B testing, blue/green deployments, and automated retraining triggers via Step Functions/Lambda/EventBridge.</li><li>Optimize performance, cost, and scalability: use Spot/Reserved Instances, SageMaker Savings Plans, Inferentia/Trainium accelerators, serverless options (Lambda for inference), elastic training, checkpointless training, and cost governance for Bedrock/SageMaker usage.</li><li>Ensure security, compliance, and responsible AI: implement IAM roles, encryption (KMS), VPC endpoints, Bedrock Guardrails for PII/toxicity, model explainability (SageMaker Clarify), bias detection, and audit logging.</li><li>Collaborate with data engineers, data scientists, product teams, and stakeholders to translate business problems into ML solutions (e.g., predictive analytics, recommendation systems, anomaly detection, computer vision, NLP, agentic workflows).</li><li>Mentor junior engineers, conduct code/model reviews, lead design sessions, and drive adoption of new AWS AI/ML features (e.g., Nova Forge, agentic AI patterns).</li><li>Troubleshoot production issues, perform root-cause analysis on model degradation, latency, or cost spikes, and maintain high availability/reliability for mission-critical ML workloads.</li><li>Stay current with AWS re:Invent announcements, evaluate emerging services (e.g., Bedrock Agents, SageMaker integrations with EMR/Athena), and contribute to internal best-practice documentation.</li><li></li></ul><p>Remote: Denver, CO<br> </p>
