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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
This role is responsible for leading the use of data to make decisions, with a focus on supporting the Casualty business through predictive modeling and machine learning solutions.
This includes the development and recommendation of new machine learning predictive modeling algorithms, the coding/development of tools that use machine learning/predictive modeling to make business decisions, searching for and integrating new data, both internal and external, that improves our modeling and machine learning results and ultimately our decisions, and discovery of new business problems that can be solved through the use of machine learning/predictive modeling.
The role is responsible for assisting in the mentorship and development of junior data scientists. This role is also accountable for informing the strategy of the department and will begin to manage complex projects as an individual contributor.
Key Responsibilities
- Uses best practices to develop complex statistical and machine learning techniques to build models that address business needs, including Casualty\-related outcomes such as claim severity, disputes, settlement strategy, and operational efficiency.
- Reviews, evaluates, communicates and makes recommendations on the appropriateness of modeling techniques and results to the team, leadership and stakeholders to ensure these are well understood and incorporated into business processes. Develops and executes communication strategy, keeps stakeholders informed, and influences business partners and senior leaders on the effectiveness of machine learning/predictive modeling to solve business problems, including the ability to explain technical information clearly to non\-technical partners.
- Utilizes effective project planning techniques to break down complex machine learning/predictive modeling and/or development projects into tasks, manages scope of projects, develops project plans, and ensures deadlines are kept. Manages analytical and modeling projects with a small number of team members and is expected to work independently with customers after initial onboarding.
- Supports, mentors, and coaches junior data scientists and team members as needed.
- Collaborates with and influences business units, including design teams and Casualty partners, to improve the effectiveness of business decisions and business results through designing, building, and partnering to implement machine learning/predictive models. Effectively understands business problems and requirements to identify the optimal modeling approach and build alignment within the department.
- Identifies languages and tools that can bring efficiencies or needed techniques to the team, including Python, SQL, R, and preferred exposure to AWS or Azure cloud environments. Identifies and develops solutions that use new areas of data, research and models, and develops data sources that can solve business problems.
Essential Skills
- 4\+ years of data science, machine learning, predictive modeling, and/or querying data experience
- Hands\-on experience with Python, SQL, and/or R
- Experience building or applying machine learning/predictive models to solve business problems
- Ability to query data and work with databases/data sources
- Ability to explain technical information clearly to non\-technical stakeholders
- Ability to work independently with customers after initial onboarding
Desired Qualifications
- Master’s Degree. In lieu of education requirements, an equivalent combination of education and experience may be considered.
- Must be able to work in Eastern or Central time zone.
Supervisory Responsibilities
- This job does not have supervisory duties.
Travel
- Must be able to travel up to 10% for onsite meetings.
*This position is not available for California, Alaska, Hawaii, Washington, or Puerto Rico residents.*
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Skills
AWS Cloud Computing, Effective Presentations, Leadership Communication, Machine Learning (ML), Predictive Modeling, Python (Programming Language), Querying Data, R Programming, Structured Query Language (SQL), Windows Azure CloudCompensation
Compensation offered for this role is 134,000\.00 \- 209,750\.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.
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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.
Allstate provides a comprehensive technology setup, including a laptop, monitors, headset, keyboard, and mouse. Employees eligible to work from home also receive a monthly connectivity reimbursement to help offset internet costs.
When working from home, you must have a dedicated, private workspace free from distractions, along with appropriate desk and seating. Reliable internet is required, with minimum speeds of 50 MB download and 5 MB upload.
Salary Context
This $134K-$209K 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 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 $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 ($171K) sits 11% below the category median. Disclosed range: $134K to $209K.
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.
Allstate Insurance AI Hiring
Allstate Insurance has 10 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer. Based in Remote, US. Compensation range: $80K - $290K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 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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