Principal Data Scientist (AI)- REMOTE (US)

Description

• Create explainability frameworks (SHAP/LIME) and monitoring dashboards ensuring transparency and regulatory adherence
• Develop predictive models for compliance risk scoring, regulatory change impact, anomaly detection, and time-series forecasting
• Write production-quality Python code for data processing, feature engineering, API development (FastAPI/Flask), and ETL/ELT workflows
• Lead A/B experiments and product analytics to measure AI feature impact and drive data-driven decision-making
• Design agentic AI systems that autonomously handle compliance workflows, document review, regulatory mapping, and multi-step reasoning tasks
• Build and deploy Generative AI features using foundation models (AWS Bedrock, OpenAI, Anthropic Claude) and RAG architectures with vector databases for compliance document understanding
• Build end-to-end MLOps pipelines for model training, deployment, monitoring, versioning, and automated retraining with drift detection
• Implement comprehensive LLM evaluation frameworks with automated pipelines, custom metrics, benchmark datasets, and safety guardrails ensuring regulatory compliance
• Collaborate with cross-functional teams to translate business needs into ML solutions and communicate insights to stakeholders

Requirements

• Understanding of ethical AI and building trustworthy, explainable systems for regulated environments
• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, or related field (PhD preferred)
• Familiarity with big data tools (Spark, Databricks, Snowflake), orchestration (Airflow, Kubeflow), and monitoring tools (Datadog, Prometheus)
• Experience working in agile environments with Jira
• Experience with LLM fine-tuning, document processing libraries, multi-modal AI, or distributed training
• Experience with agentic AI frameworks (LangGraph, LangChain, AutoGen, CrewAI)
• AWS ML certifications or similar credentials
• Knowledge of Life Sciences/regulated industries (FDA, EMA, ISO, GxP) and compliance management systems
• Understanding of ML governance, bias detection, model risk management, and data privacy regulations (GDPR, CCPA, HIPAA)
• Track record of deploying ML systems processing large-scale datasets with proper monitoring and governance
• 7+ years in data science, ML engineering, or related roles
• Strong communication skills explaining complex models to technical and non-technical audiences
• 3+ years building NLP/generative AI applications and implementing MLOps in production
• Ability to work independently and collaboratively in fast-paced environments
• Proven ability to convert POCs into production-grade solutions

Benefits

Training + Development

Interview process

Visa Sponsorship

Security clearance