ETQ
Principal Data Scientist (AI) – Remote (US)
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.
Responsibilities
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.
Qualifications
7+ years in data science, ML engineering, or related roles.
3+ years building NLP/Generative AI applications and implementing MLOps in production.
Python (5+ years): Production‑level experience with Pandas, NumPy, scikit‑learn, XGBoost, TensorFlow/PyTorch, Hugging Face Transformers, FastAPI/Flask, MLflow, and pytest.
SQL: Advanced proficiency with complex queries, window functions, and optimization.
Strong foundation in supervised/unsupervised learning, deep learning, document understanding, text classification, and semantic analysis.
Hands‑on experience with foundation models, prompt engineering, RAG architectures, and vector databases (Pinecone, Weaviate, Chroma).
End‑to‑end experience with ML pipelines, experiment tracking (MLflow, W&B), model versioning, feature stores, drift detection, CI/CD for ML, and Docker containerization.
Experience with evaluation frameworks (RAGAS, DeepEval), custom metrics, benchmark datasets, and human‑in‑the‑loop validation.
Experience with AWS services including SageMaker, Bedrock, S3, Lambda, EC2, and CloudWatch.
Strong foundation in statistics, A/B testing, causal inference, and experimental design.
Proficiency with Tableau, Power BI, or Python visualization libraries.
Preferred: Experience with agentic AI frameworks (LangGraph, LangChain, AutoGen, CrewAI). Knowledge of 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).
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 (preferred).
Key Competencies
Strong communication skills explaining complex models to technical and non‑technical audiences.
Ability to work independently and collaboratively in fast‑paced environments.
Proven ability to convert POCs into production‑grade solutions.
Understanding of ethical AI and building trustworthy, explainable systems for regulated environments.
Hexagon will NOT be able to provide visa sponsorship at any time during employment. If you will require visa sponsorship at any point in time, please refrain from applying. Everyone is welcome : We believe that diverse and inclusive teams are critical to the success of our people and our business. Everyone is welcome—as an inclusive workplace, we do not discriminate. We embrace differences and are fully committed to creating equal opportunities, an inclusive environment, and fairness for all.
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Responsibilities
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.
Qualifications
7+ years in data science, ML engineering, or related roles.
3+ years building NLP/Generative AI applications and implementing MLOps in production.
Python (5+ years): Production‑level experience with Pandas, NumPy, scikit‑learn, XGBoost, TensorFlow/PyTorch, Hugging Face Transformers, FastAPI/Flask, MLflow, and pytest.
SQL: Advanced proficiency with complex queries, window functions, and optimization.
Strong foundation in supervised/unsupervised learning, deep learning, document understanding, text classification, and semantic analysis.
Hands‑on experience with foundation models, prompt engineering, RAG architectures, and vector databases (Pinecone, Weaviate, Chroma).
End‑to‑end experience with ML pipelines, experiment tracking (MLflow, W&B), model versioning, feature stores, drift detection, CI/CD for ML, and Docker containerization.
Experience with evaluation frameworks (RAGAS, DeepEval), custom metrics, benchmark datasets, and human‑in‑the‑loop validation.
Experience with AWS services including SageMaker, Bedrock, S3, Lambda, EC2, and CloudWatch.
Strong foundation in statistics, A/B testing, causal inference, and experimental design.
Proficiency with Tableau, Power BI, or Python visualization libraries.
Preferred: Experience with agentic AI frameworks (LangGraph, LangChain, AutoGen, CrewAI). Knowledge of 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).
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 (preferred).
Key Competencies
Strong communication skills explaining complex models to technical and non‑technical audiences.
Ability to work independently and collaboratively in fast‑paced environments.
Proven ability to convert POCs into production‑grade solutions.
Understanding of ethical AI and building trustworthy, explainable systems for regulated environments.
Hexagon will NOT be able to provide visa sponsorship at any time during employment. If you will require visa sponsorship at any point in time, please refrain from applying. Everyone is welcome : We believe that diverse and inclusive teams are critical to the success of our people and our business. Everyone is welcome—as an inclusive workplace, we do not discriminate. We embrace differences and are fully committed to creating equal opportunities, an inclusive environment, and fairness for all.
#J-18808-Ljbffr