What are the responsibilities and job description for the Principal Data Scientist (AI)- REMOTE (US) position at Hexagon Asset Lifecycle Intelligence?
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.
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 & ModelOps: 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
LLM Evaluation: Experience with evaluation frameworks (RAGAS, DeepEval), custom metrics, benchmark datasets, and human-in-the-loop validation
Cloud & AWS: Experience with AWS services including SageMaker, Bedrock, S3, Lambda, EC2, and CloudWatch
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
About Hexagon
Hexagon is a global leader in digital reality solutions, combining sensor, software and autonomous technologies. We are putting data to work to boost efficiency, productivity, quality and safety across industrial, manufacturing, infrastructure, public sector, and mobility applications.
Hexagon’s Asset Lifecycle Intelligence division helps clients design, construct, and operate more profitable, safe, and sustainable industrial facilities. We empower customers to unlock data, accelerate industrial project modernization and digital maturity, increase productivity, and move the sustainability needle.
Our technologies help produce actionable insights that enable better decision-making and intelligence across the asset lifecycle of industrial projects, leading to improvements in safety, quality, efficiency, and productivity, which contribute to Economic and Environmental Sustainability.
Hexagon (Nasdaq Stockholm: HEXA B) has approximately 25,000 employees in 50 countries and net sales of approximately 5.4bn EUR. Learn more at hexagon.com and follow us @HexagonAB.
Why work for Hexagon?
At Hexagon, if you can see it, you can do it. Hexagon’s Asset Lifecyle Intelligence division puts their trust in you so that you can bring your ideas to life. We have emerged as one of the most engaged and enabled workplaces*. We are committed to creating an environment that is truly supportive by providing the resources you need to fully support your ambitions, no matter who you are or where you are in the world.
At Hexagon, 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. In fact, we embrace differences and are fully committed to creating equal opportunities, an inclusive environment, and fairness for all.
Respect is the cornerstone of how we operate, so speak up and be yourself. You are valued here.
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
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 & ModelOps: 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
LLM Evaluation: Experience with evaluation frameworks (RAGAS, DeepEval), custom metrics, benchmark datasets, and human-in-the-loop validation
Cloud & AWS: Experience with AWS services including SageMaker, Bedrock, S3, Lambda, EC2, and CloudWatch
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
- Experience with agentic AI frameworks (LangGraph, LangChain, AutoGen, 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
- 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
About Hexagon
Hexagon is a global leader in digital reality solutions, combining sensor, software and autonomous technologies. We are putting data to work to boost efficiency, productivity, quality and safety across industrial, manufacturing, infrastructure, public sector, and mobility applications.
Hexagon’s Asset Lifecycle Intelligence division helps clients design, construct, and operate more profitable, safe, and sustainable industrial facilities. We empower customers to unlock data, accelerate industrial project modernization and digital maturity, increase productivity, and move the sustainability needle.
Our technologies help produce actionable insights that enable better decision-making and intelligence across the asset lifecycle of industrial projects, leading to improvements in safety, quality, efficiency, and productivity, which contribute to Economic and Environmental Sustainability.
Hexagon (Nasdaq Stockholm: HEXA B) has approximately 25,000 employees in 50 countries and net sales of approximately 5.4bn EUR. Learn more at hexagon.com and follow us @HexagonAB.
Why work for Hexagon?
At Hexagon, if you can see it, you can do it. Hexagon’s Asset Lifecyle Intelligence division puts their trust in you so that you can bring your ideas to life. We have emerged as one of the most engaged and enabled workplaces*. We are committed to creating an environment that is truly supportive by providing the resources you need to fully support your ambitions, no matter who you are or where you are in the world.
- In the recently concluded workplace effectiveness survey by Korn Ferry, a global HR advisory firm, Hexagon, Asset Lifecycle Intelligence division has emerged as one of the most Engaged and Enabled workplaces, when compared to similar organizations that Korn Ferry partners with.
At Hexagon, 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. In fact, we embrace differences and are fully committed to creating equal opportunities, an inclusive environment, and fairness for all.
Respect is the cornerstone of how we operate, so speak up and be yourself. You are valued here.