Position: Principal Data Scientist (AI)- REMOTE (US), Sales Principal Data Scientist (AI) - REMOTE (US)

Job Location (Short): Houston, Texas-USA | Madison, Alabama-USA | Roanoke, Virginia-USA

Workplace Type: Remote

Business Unit: ALI-ETQ

Req. Responsibilities

Hexagon's ETQ division is seeking a hands‑on Data Scientist to build predictive models, implement Generative AI and Agentic AI features, and architect data‑driven solutions for our document‑based compliance management platform. This role requires a technical expert who can develop, deploy, and maintain ML systems in production environments.

• Build and deploy Generative AI features using foundation models (AWS Bedrock, OpenAI, Anthropic Claude) and RAG architectures with vector databases for compliance document understanding
• Design agentic AI systems that autonomously handle compliance workflows, document review, regulatory mapping, and multi‑step reasoning tasks
• Implement comprehensive LLM evaluation frameworks with automated pipelines, custom metrics, benchmark datasets, and safety guardrails ensuring regulatory compliance
• Build end‑to‑end MLOps pipelines for model training, deployment, monitoring, versioning, and automated retraining with drift detection
• 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
• Create explainability frameworks (SHAP/LIME) and monitoring dashboards ensuring transparency and regulatory adherence
• Collaborate with cross‑functional teams to translate business needs into ML solutions and communicate insights to stakeholders

Required Skills & Experience

• Python (5+ years): Production‑level experience with Pandas, Num Py, scikit‑learn, XGBoost, Tensor Flow/PyTorch, Hugging Face Transformers, FastAPI/Flask, MLflow, and pytest
• SQL: Advanced proficiency with complex queries, window functions, and optimization
• Machine Learning & NLP: Strong foundation in supervised/unsupervised learning, deep learning, document understanding, text classification, and semantic analysis
• Generative AI & LLMs: Hands‑on experience with foundation models (GPT, Claude, Llama), prompt engineering, RAG architectures, and vector databases (Pinecone, Weaviate, Chroma)
• MLOps & Model Ops: End‑to‑end experience with ML pipelines, experiment tracking (MLflow, W&B), model versioning, feature stores, drift detection, bolthires/CD for ML, and Docker containerization
• LLM Evaluation: Experience with evaluation frameworks (RAGAS, Deep Eval), custom metrics, benchmark datasets, and human‑in‑the‑loop validation
• Cloud & AWS: Experience with AWS services including Sage Maker, Bedrock, S3, Lambda, EC2, and Cloud Watch
• Statistics & Experimentation: Strong foundation in statistics, A/B testing, causal inference, and experimental design
• Visualization: Proficiency with Tableau, Power BI, or Python visualization libraries

Education / Qualifications

Experience & Education

• 7+ years in data science, ML engineering, or related roles
• 3+ years building NLP/generative AI applications and implementing MLOps in production
• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, or related field (PhD preferred)
• Track record of deploying ML systems processing large‑scale datasets with proper monitoring and governance

Preferred Qualifications

• Experience with agentic AI frameworks (Lang Graph, Lang Chain, Auto Gen, CrewAI)
• Knowledge of Life Sciences/regulated industries (FDA, EMA, ISO, GxP) and compliance management systems
• Familiarity with big data tools (Spark, Databricks, Snowflake), orchestration (Airflow, Kubeflow), and monitoring tools (Datadog, Prometheus)
• Experience with LLM fine‑tuning, document processing libraries, multi‑modal AI, or distributed training
• Understanding of ML governance, bias detection, model risk management, and data privacy regulations (GDPR, CCPA, HIPAA)
• Experience working in agile environments with Jira
• AWS ML certifications or similar credentials

Key Competencies

• Strong communication skills explaining complex models to technical and non-technical stakeholders