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About This Role
Location: Remote
Work Type: Full Time Regular
Job No: 504903
Categories: Information Technology
Application Closes: Open Until Filled
2026\-06\-08
The Emerging and Strategic Solutions ART is looking for a Data Scientist specializing in Generative AI who will be at the forefront of transforming our business segment through the design, development, and deployment of production AI applications. You will architect and ship agentic AI systems that transform how our operational areas work, including applications that can reason over business context, take action through tools and integrations, and automate complex workflows end to end. This role calls for a hands\-on builder with deep experience taking LLM applications from prototype to production, supported by strong full\-stack engineering, MLOps, and cloud infrastructure skills. This position demands a proactive problem solver who thrives in ambiguity and can adapt swiftly to changing priorities, making a significant.
WHAT WE CAN OFFER YOU:
- $130,000 to $150,000, eligible for annual bonus, as applicable.
- 401(k) plan with a 2% company contribution and 6% company match.
- Work\-life balance with vacation, personal time and paid holidays. See our benefits and perks page for details.
- Applicants for this position must not now, nor at any point in the future, require sponsorship for employment.
WHAT YOU’LL DO:
- Lead the design, development, and deployment of Generative AI applications by immersing yourself in the needs of operational areas. Build empathy for business challenges and apply engineering best practices to deliver innovative, robust, and reliable AI solutions.
- Lead the design, development, and deployment of agentic Generative AI applications by immersing yourself in the needs of operational areas. Build empathy for business challenges and apply engineering best practices to deliver innovative, robust, and reliable AI solutions.
- Drive the end\-to\-end lifecycle of AI/ML projects, from ideation and experimentation through production deployment. Use prompt engineering, data versioning, systematic experimentation, and A/B testing to optimize performance.
- Develop and maintain secure, scalable infrastructure on AWS, with a focus on cloud resource management, cost optimization, and compliance. Prioritize security and fairness throughout the development and deployment process to protect our customers and uphold ethical standards.
- Collaborate on systems design and integration, ensuring seamless interoperability between AI systems and both modern and legacy front\-end/back\-end platforms. Maintain CI/CD pipelines and automated testing frameworks to support continuous delivery and operational reliability.
- Partner with business and technical stakeholders to identify high\-impact use cases for generative AI in underwriting, customer service, claims, and risk modeling, ensuring alignment with strategic goals.
- Implement and promote MLOps best practices, enabling reproducibility, scalability, and model governance throughout the development lifecycle.
- Lead delivery using agile methodologies, incorporating continuous feedback and fostering a culture of experimentation and iterative improvement. Own your solutions from prototype through production.
- Stay informed about emerging trends in Generative AI, cloud\-native technologies, model governance, and responsible AI, and apply them to deliver long\-term business value.
- Mentor junior data scientists and engineers, promoting best practices in AI development, documentation, and cross\-functional collaboration.
WHAT YOU’LL BRING:
- Bachelor’s degree in Computer Science, Engineering, or analytical fields (Mathematics, Data Science, etc.), or equivalent experience, with at least 5 years deploying and supporting full\-stack applications in enterprise environments, and several years of experience integrating AI, MLOps into full\-stack applications.
- Experienced in designing and shipping production Generative AI\-based applications with agentic architectures, with strong skills in prompt engineering, orchestration frameworks (e.g., LangChain, LangGraph, or similar), tool/function calling, structured outputs, model selection, and LLM evaluation.
- Experienced in full\-stack development (backend, frontend, and database technologies)withexpertise in technologies such as Vue and TKG, and strong skills in Python, TypeScript, git, SQL, CI/CD pipelines, automated testing, and DevOps best practices.
- Experienced in AWS services, particularly Bedrock, SageMaker, S3, Lambda, and infrastructure as code (CDK). Extensive experience applying software engineering design patterns and enterprise application architecture principles to build secure, scalable, maintainable, and cost\-optimized cloud\-native applications.
- Experienced in data science, machine learning techniques, and data engineering. Skilled ataddressing fairness in AI development and applying experimentation and A/B testing methodologies to empirically evaluate and improve model performance.
- Strong communicator and collaborator, experienced at building partnerships in remote and ever\-changing environments. Resilient and resourceful problem solver, proactive at overcoming obstacles to achieve goals. Comfortable with ambiguity and change, demonstrating high learning agility, adapting quickly to shifting priorities, and bringing clarity to undefined problems.
PREFERRED:
- Advanced degree in an analytical field.
- Familiarity with single or multi\-cloud agentic architecture for building LLM\-based applications.
- Proven experience with MLOps andimplementing automated pipelines for model and algorithm training, monitoring, versioning, deployment, and scalingin production environments.
Why Join Us?
- Here, your skills spark progress. You’ll solve meaningful problems, collaborate with passionate teammates, and grow in a space where innovation is the norm—not the exception.
- We value diverse experience, skills, and passion for innovation. If your experience aligns with the listed requirements
- If you have questions about your application or the hiring process, email our Talent Acquisition area at [email protected]. Please allow at least one week from time of applying if you are checking on the status.
- Stay Safe from Job Scams
Mutual of Omaha only accepts applications from mutualofomaha.com/careers. Legitimate communications will come from '@mutualofomaha.com.' We never request sensitive information or extend job offers without conducting interviews. For more details, check our Hiring FAQs. Stay alert for scams and apply securely!
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An inclusive culture
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Surround yourself with an authentic and inclusive culture. Your strengths and differences will be valued and celebrated by a diverse community of co‑workers.
Salary Context
This $130K-$150K range is below the median for Data Scientist roles in our dataset (median: $157K across 236 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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Mutual of Omaha, 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 $198,000 based on 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($140K) sits 29% below the category median. Disclosed range: $130K to $150K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Mutual of Omaha AI Hiring
Mutual of Omaha has 2 open AI roles right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $130K - $150K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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