Interested in this Data Scientist role at Allstate Insurance?
Apply Now →Skills & Technologies
About This Role
At Allstate, great things happen when our people work together to protect families and their belongings from life’s uncertainties. And for more than 90 years, our innovative drive has kept us a step ahead of our customers’ evolving needs. From advocating for seat belts, air bags and graduated driving laws, to being an industry leader in pricing sophistication, telematics, and, more recently, device and identity protection.
Job Description
Domain: Enterprise Risk Management and AI Governance
The team, its scope \& environment
- The AI Risk, Governance and Research team is re\-imagining AI governance and risk management using modern data science and AI capabilities. Instead of treating governance as a one\-time checkpoint, the goal is to build continuous, data driven oversight, systems that monitor, assess, and quantify risk as AI solutions evolve in real time. The focus is on understanding what risks matter, how those risks are managed across the industry, and how best practices translate into practical, measurable signals. Team members use quantitative thinking and data science techniques to surface evidence of risk from a wide range of information sources, both internal and external to Allstate.
- You’ll join a core team that is intentionally structured to include a balanced mix of entry level, mid\-career, senior, and expert level contributors, creating an environment that supports both collaboration and deep technical and functional expertise growth.
- Because the team sits within the broader Enterprise Risk Org, the work is inherently crosscutting and high visibility. You’ll collaborate with other data scientists, engineers, and governance partners who are shaping how AI is evaluated, monitored, and trusted at scale, well beyond a single product or application.
- As a team member, you’ll contribute to the development and deployment of machine learning and AI solutions, with exposure to observability, security, and efficient LLM utilization.
Data Scientist\- Overall work scope
- Reporting to the Senior Manager, Data Science, you will support the development and maintenance of data driven agentic solutions, working under the guidance of senior team members across the full lifecycle, from data collection and exploratory analysis to model/agent development, deployment, and monitoring.
- You will build foundational experience designing and implementing AI agents and cloud\-based AI solutions, while learning how to maintain scalable AI systems in production environments.
- You’ll collaborate across teams to learn and apply established best practices and reusable components, helping ensure consistent and high\-quality execution of agents.
Our ideal candidate:
- You bring solid data science fundamentals and don’t stop at exploratory analysis. You’re interested in testing hypotheses, evaluating tradeoffs, and refining approaches to arrive at solutions that can be realistically delivered within real world constraints.
- You care about outcomes, not just insights, and are comfortable moving work forward with structure and follow through.
- You understand how to prioritize, iterate, and deliver within timelines while maintaining analytical rigor.
Day in the life (essential functions):
- Learn and apply LLMs, programming languages, and tools used by the team to improve efficiency and solve business problems.
- Assist in exploring new data sources, research, and models under the direction of senior team members.
- Supports project planning efforts by breaking down AI/predictive modeling and development tasks into manageable subtasks, tracking progress, and meeting assigned deadlines. Execute analytical and modeling tasks as part of a larger project team.
- Apply standard best practices to develop statistical and machine learning techniques to build models that address business needs and improve data quality and decision making.
- Review and discuss AI/modeling techniques and results with peers and mentors, clearly communicating findings and incorporating feedback.
- Contributes to stakeholder communications by preparing summaries, documentation, and analysis to support senior team members in influencing business partners and leaders.
- Work with guidance to understand business problems and requirements and help identify appropriate modeling approaches.
- Assist in developing prototypes and frameworks that integrate data and machine learning/predictive modeling into business decision making processes.
Required Qualifications:
- 0 to 2 years of relevant experience
Preferred education, skills and experiences:
- Bachelors or master's in data science, STEM sciences or related field
- One plus year(s) of professional/practical experience as Data Scientist, Machine Learning Engineer, Applied Research professional, or relevant and impactful business internships experience
- Prior experience or high interest in Enterprise Risk Management or AI governance related work
- Prior experience working in Insurance or related industries
- Strong theoretical exposure or practical experience with Infrastructure as Code (IaC) frameworks to provision and manage cloud resources
- Strong understanding of CI/CD pipelines, containerization (Docker), observability tools, and cloud security practices.
- Exposure or practical experiences in Azure Foundry, or AWS Bedrock
- Introductory experience or strong interest in AI agent development using at least one framework (e.g., Azure AF, AWS Strands, Google ADK, LangGraph, OpenAI Agents SDK).
- Familiarity with building no\-code/low\-code agents using M365\.
Synonymic titles: Applied Data Scientist, Software Engineer\- Data Science.
Skills
AI Agents, AI Governance, Applied Machine Learning, Machine Learning (ML), Machine Learning Algorithms, Predictive Analytics, Predictive Modeling, Python (Programming Language), PyTorchCompensation
Compensation offered for this role is 85,000\.00 \- 145,075\.00 annually and is based on experience and qualifications.
The candidate(s) offered this position will be required to submit to a background investigation.
Joining our team isn’t just a job — it’s an opportunity. One that takes your skills and pushes them to the next level. One that encourages you to challenge the status quo. One where you can shape the future of protection while supporting causes that mean the most to you. Joining our team means being part of something bigger – a winning team making a meaningful impact.
Allstate generally does not sponsor individuals for employment\-based visas for this position.
Effective July 1, 2014, under Indiana House Enrolled Act (HEA) 1242, it is against public policy of the State of Indiana and a discriminatory practice for an employer to discriminate against a prospective employee on the basis of status as a veteran by refusing to employ an applicant on the basis that they are a veteran of the armed forces of the United States, a member of the Indiana National Guard or a member of a reserve component.
For jobs in San Francisco, please click “here” for information regarding the San Francisco Fair Chance Ordinance.
For jobs in Los Angeles, please click “here” for information regarding the Los Angeles Fair Chance Initiative for Hiring Ordinance.
To view the “EEO Know Your Rights” poster click “here”. This poster provides information concerning the laws and procedures for filing complaints of violations of the laws with the Office of Federal Contract Compliance Programs.
To view the FMLA poster, click “here”. This poster summarizing the major provisions of the Family and Medical Leave Act (FMLA) and telling employees how to file a complaint.
It is the Company’s policy to employ the best qualified individuals available for all jobs. Therefore, any discriminatory action taken on account of an employee’s ancestry, age, color, disability, genetic information, gender, gender identity, gender expression, sexual and reproductive health decision, marital status, medical condition, military or veteran status, national origin, race (include traits historically associated with race, including, but not limited to, hair texture and protective hairstyles), religion (including religious dress), sex, or sexual orientation that adversely affects an employee's terms or conditions of employment is prohibited. This policy applies to all aspects of the employment relationship, including, but not limited to, hiring, training, salary administration, promotion, job assignment, benefits, discipline, and separation of employment.
Salary Context
This $85K-$145K range is in the lower quartile for Data Scientist roles in our dataset (median: $166K across 345 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 26,159 AI roles we're tracking, Data Scientist positions make up 2% of the market. At Allstate Insurance, 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 $204,700 based on 441 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($115K) sits 44% below the category median. Disclosed range: $85K to $145K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Allstate Insurance AI Hiring
Allstate Insurance has 13 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer, AI Product Manager. Positions span Remote, US, Charlotte, NC, US. Compensation range: $66K - $209K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.