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About This Role
Location Amelia, United States Job type Full\-Time Work mode Remote Job level Professional Job ID 13431 Company American Modern Insurance Group Employment type Regular Area of expertise Data, BI \& Analytics
All locations Amelia, United States; Akron, United States; Ann Arbor, United States; Arlington, United States; Atlanta, United States; Bloomington, United States; Buffalo, United States; Charleston, United States; Charlotte, United States; Chattanooga, United States; Chicago, United States; Cincinnati, United States; Cleveland, United States; Columbus, United States; Dayton, United States; Fishers, United States; Fort Lauderdale, United States; Houston, United States; Indianapolis, United States; Jacksonville, United States; Kansas City, United States; Lexington\-Fayette, United States; Memphis, United States; Philadelphia, United States;
Drive the future of AI‑powered decision‑making by leading sophisticated machine learning and GenAI solutions that shape strategy and outcomes.
American Modern Insurance Group, Inc., a Munich Re company, is a widely recognized specialty insurance leader that delivers products and services for residential property – such as manufactured homes and specialty dwellings – and the recreational market, including boats, personal watercraft, classic cars, and more. We provide specialty product solutions that cover what the competition often can’t. We write admitted products in all 50 states and have a premium volume of $2\.2 billion.
Headquartered in Amelia, Ohio, and with associates located across the United States, we are part of Munich Re’s Global Specialty Insurance division. Our employees receive boundless opportunity to grow their careers and make a difference every day \- all in a flexible environment that helps them succeed both at work and at home.
Join our dynamic, centralized Data Science team as we execute our AI/ML roadmap! We focus on developing and maintaining predictive models that support all domains across the business. In this role, you'll collaborate closely with senior data scientists to build and support AI/ML and Gen AI models. You will test models to ensure efficacy and compliance before deployment, work hand\-in\-hand with ML Engineers to deploy models and verify system performance, and monitor models post\-deployment, making adjustments as needed. Join us in building AI/ML models that truly drive business value and make a measurable impact.
Roles \& Responsibilities:
- Build and deploy AI/ML models with general guidance.
- Act as a liaison between the ML Engineers and Data Science team, managing AI/ML model deployments and conducting testing.
- Participate in the development of modeling presentations and present results/topics to stakeholders (internal \& external) and management as requested.
- Concentrate on loosely defined problems which require application of creative approaches. Contribute to projects that yield actionable insights the business can use to increase customer satisfaction, policy growth, retention, and profitability.
- Complete required data preparation, including data input with guidance. Identify data anomalies and seek guidance on necessary adjustments.
- Identify data and operational issues requiring specialized analytics attention, data scrubbing, and modeling techniques.
- Document and communicate assumptions, results, and alternatives to team members and internal stakeholders with guidance.
- Create and maintain modeling best practices and new techniques in coordination with internal and external business partners.
- Explain advanced analytic and modeling procedures in the language that audiences with no predictive analytics training can easily find connections to. Understand stakeholders’ requests and hidden issues and explain possible solutions in their language.
- Schedule and prioritize workload demands. Maintain very good organization skills. Complete technical peer reviews as needed.
- Maintain strong interpersonal skills \- listen deeply, ask correct questions, take notes, complete professional oral and written business communications (including email communications), presentations, and build business relationships in order to better understand insurance company processes and functions. Established relationships with multiple areas of interaction.
Required Technical Skills:
- Proficiency in statistical programming languages such as R, Python, SAS, or similar, alongside general programming and SQL knowledge.
- Demonstrated experience building and deploying statistical and machine learning models to address business challenges.
- Skilled in data wrangling, quality evaluation, transformation, and cleaning for analysis.
- Experience with AI/ML model testing, validation, deployment, and integration into CI/CD pipelines.
- Familiarity with tools such as MLflow for model experimentation and registration, Git for version control, and agile frameworks using tools like Azure DevOps.
Skills / Knowledge / Experience:
- Develop and project manage modeling projects and statistical analyses.
- Effectively communicate results to business partners and help drive business decisions.
- Participate in creation and discussion of best practices and propose new techniques.
- Mentor junior team members.
- Data Scientist: 2 or more years of data science/predictive analytics experience in (re)insurance or 4 or more years predictive modeling experience in another industry.
- Sr. Data Scientist: 4 or more years of data science/predictive analytics experience in (re)insurance or 6 or more years predictive modeling experience in another industry.
Education, Certifications \& Designations:
- Master’s degree in Business Analytics, Statistics, Computer Science, or Statistics/Math and Economics double major, or Statistics/Math and Computer Science double major or Statistics/Math and Operation Research double major
We are proud to offer our employees, their domestic partners, and their children, a wide range of insurance benefits:
- Two options for your health insurance plan (PPO or High Deductible)
- Prescription drug coverage (included in your health insurance plan)
- Vision and dental insurance plans
- Short and Long Term Disability coverage
- Supplemental Life and AD\&D plans that you can purchase for yourself and dependents (includes spouse/domestic partner and children)
- Voluntary Benefit plans that supplement your health and life insurance plans (Accident, Critical Illness and Hospital Indemnity)
In addition to the above insurance offerings, our employees also enjoy:
- A robust 401k plan with up to a 5% employer match
- A retirement savings plan that is 100% company funded
- Paid time off that begins with 24 days each year, with more days added when you celebrate milestone service anniversaries
- Eligibility to receive a yearly bonus as a Munich Re employee
- A variety of health and wellness programs provided at no cost
- A hybrid environment that gives you a choice in where and how you get work done
- A corporately subsidized on\-site cafeteria as well as a We Proudly Serve coffee shop
- An on\-site complimentary workout facility as well as walking trails on campus grounds
- On\-site wellness center complete with nurse practitioner
- Financial assistance for adoptions and infertility treatment
- Paid time off for eligible family care needs
- Tuition assistance and educational achievement bonuses
- Free parking
- A corporate matching gifts program that further enhances your charitable donation
- Paid time off to volunteer in your community
At American Modern, a subsidiary of Munich Re, we see Diversity and Inclusion as a solution to the challenges and opportunities all around us. Our goal is to foster an inclusive culture and build a workforce that reflects the customers we serve and the communities in which we live and work. We strive to provide a workplace where all of our colleagues feel respected, valued and empowered to achieve their very best every day. We recruit and develop talent with a focus on providing our customers the most innovative products and services.
We are an equal opportunity employer. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Applicants requiring employer sponsorship of a visa will not be considered for this position.
\#LI\-SF1
Your Benefits
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With us, you get more than just an exciting job. Enjoy a wide range of employee benefits tailored to your wellbeing and development. Please note that regional differences may apply.
Competitive Salary
We provide fair and competitive compensation that reflects your performance and commitment.
Company performance\-based Incentives
In addition to your salary, our variable compensation approach allows you to share in Munich Re's success.
Recognition and Special Rewards
We recognise outstanding individual contributions through a variety of targeted rewards and incentives.
Retirement Planning
We support your long\-term financial wellbeing with retirement solutions aligned with local regulations.
Inclusive Workplace Culture
We foster a respectful, inclusive, and values\-driven environment.
Learning \& Development
We offer tailored learning opportunities with a strong focus on core skills and business\-critical knowledge.
Work\-Life\-Balance
Supporting your ability to balance family, leisure, and your career.
Health \& Wellbeing
We support your physical and mental health through a range of programs and activities in each location.
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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At American Modern Insurance Group, 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 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
American Modern Insurance Group AI Hiring
American Modern Insurance Group has 1 open AI role right now. They're hiring across Data Scientist. Based in Amelia, OH, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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