Principal Data Scientist

Company Details

Driven by a commitment to collaboration, DNA partners with our customers and Operating Units by providing comprehensive solutions that not only address the challenge at hand, but proactively plan for the “What’s Next” in our industry and beyond.  Our mission is to drive transformation and provide exceptional capabilities and service to the operating units. DNA Enterprise Reporting generates meaningful and measurable value by delivering insights for our customers, partners, and shareholders using data and analytics.  

Our vision is to enable operating unit profit and growth objectives by designing and delivering scalable solutions.  With a culture centered on innovation and service stewardship, DNA stands as a community of leaders with eyes toward the future -- leaders who truly care about growing not only their team members, but themselves, and take pride in their employees who shine.  DNA offers endless ways to get involved and have the chance to grow your career into a wide range of roles. Come join us as we push forward into the future of industry leading technology and service solutions.

Company URL:  https://www.berkley.com/

The company is an equal opportunity employer.

Responsibilities

We are seeking an exceptional Principal Data Scientist who is part deep technologist, part entrepreneur, and part strategic innovator. This is not a traditional analytics role, it is built for a builder. You will own the full lifecycle of high-impact AI/ML solutions, from whiteboard to production, writing substantial code and driving rigorous analysis that directly shapes enterprise decisions.

Sitting at the intersection of advanced machine learning, software engineering, and business strategy, you will architect and ship production-grade AI systems across underwriting, claims, operations, and finance.

AI Engineering & Production ML Development

• Own the code, not just the model: Design, write, test, and deploy production-grade ML and AI systems using Python, modern ML frameworks, and cloud-native tooling.
• Build generative AI & LLM-powered solutions: Architect and implement RAG pipelines, fine-tuning workflows, agentic systems, and LLM evaluation harnesses.
• Engineer scalable ML pipelines: Develop robust feature engineering, training, inference, and monitoring pipelines built for reliability and scale.
• Ship end-to-end: Take models from prototype through CI/CD into monitored production environments, including automated retraining and drift detection.

Advanced Data Science & Analytical Rigor

• Lead complex analytical investigations: Apply causal inference, Bayesian modeling, survival analysis, and simulation to solve high-stakes business problems.
• Translate ambiguity to impact: Frame undefined problems with entrepreneurial clarity: define success metrics, scope solutions, and move from question to insight at speed.
• Ensure reproducibility and rigor: Establish standards for experiment tracking, version control, and model validation aligned with enterprise governance requirements.

Architecture, Platforms & Technical Strategy

• Shape the AI/ML platform: Evaluate and recommend tools, frameworks, and cloud services (Azure ML, Databricks, MLflow, etc.) that form the backbone of enterprise AI capability.
• Establish reusable accelerators: Build and document shared libraries, templates, and design patterns that multiply team productivity across the data science community.
• Drive MLOps excellence: Define and enforce best practices for model governance, monitoring, A/B testing, and lifecycle management in production.
• Architect for the long term: Make principled trade-offs between build vs. buy, speed vs. rigor, and experimentation vs. standardization.

Entrepreneurial Innovation & Strategic Influence

• Rapidly prototype and validate: Move from idea to working proof-of-concept in days, not months using experimentation to de-risk investment before scaling.
• Influence enterprise standards: Shape the organization's model development, validation, and deployment standards as a principal-level technical authority.

Qualifications

Education

• Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, or a closely related quantitative field.
• Master's or PhD preferred

Experience

• 10+ years of hands-on experience in applied machine learning, data science, or AI engineering  not just analytics. Demonstrated track record of shipping ML models and AI systems to production, including ownership of monitoring and maintenance.
• Experience leading complex, end-to-end data science projects from problem definition through deployment and business impact measurement.
• Proven ability to influence technical direction and strategy without direct management authority.

Technical Proficiency  (Must Be Hands-On)

• Python (expert-level): NumPy, Pandas, Scikit-learn, PyTorch or TensorFlow, Hugging Face, LangChain/LlamaIndex or equivalent.
• ML Engineering: Feature stores, model registries (MLflow), experiment tracking, CI/CD for ML, containerization (Docker/Kubernetes).
• LLMs & Generative AI: Prompt engineering, RAG architecture, fine-tuning, evaluation frameworks, and agentic workflow design.
• SQL & Data Engineering: Complex query optimization, dbt or similar, working fluently with Spark or Databricks.
• Cloud Platforms: Azure ML preferred; AWS SageMaker or GCP Vertex AI experience
• Statistics & ML Foundations: Regression, classification, clustering, time-series, Bayesian methods, causal inference, and model interpretability (SHAP, LIME).
• Software Engineering Practices: Git, code review, unit testing, design patterns you write code that others can maintain.

Preferred Qualification

• Experience in financial services, insurance, or other regulated industries with model risk management requirements.
• Contributions to open-source ML projects
• Experience building and operating real-time inference systems (low-latency APIs, streaming prediction pipelines).
• Familiarity with model governance frameworks and regulatory requirements
• Experience with agentic AI systems, multi-modal models, or domain-adapted LLMs in an enterprise context.
• Background in agile/product-oriented analytics teams with sprint-based delivery.

Additional Company Details

Sponsorship Details