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About This Role
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 Senior 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.
Key Responsibilities:
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.
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* 3\-5\+ 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: We do not accept any unsolicited resumes from external recruiting agencies or firms. The company offers a competitive compensation plan and robust benefits package for full\-time regular employees which for this role include: • Base Salary Range: $150,000 – $200,000 • Eligible to participate in annual discretionary bonus. • Benefits: Health, Dental, Vision, Life, Disability, Wellness, Paid Time Off, 401(k) and Profit\-Sharing plans. The actual salary for this position will be determined by a number of factors, including the scope, complexity and location of the role; the skills, education, training, credentials and experience of the candidate; and other conditions of employment. Sponsorship Details: Sponsorship not Offered for this Role
Salary Context
This $150K-$200K range is above the median for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Berkley, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($175K) sits 9% below the category median. Disclosed range: $150K to $200K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Berkley AI Hiring
Berkley has 2 open AI roles right now. They're hiring across Data Scientist. Based in Glen Allen, VA, US. Compensation range: $200K - $300K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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