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About This Role
Our Data Science team sits at the heart of innovation, driving impactful solutions across customer experience, operational efficiency, and business growth. We are a diverse group of data scientists, machine learning engineers, and technical leaders working on high\-impact projects, including Generative AI, predictive modeling, conversational AI, and multimodal data applications.
We collaborate closely with Product, Engineering, Analytics, and Business stakeholders to solve complex problems using advanced machine learning and statistical modeling. Our team values curiosity, ownership, and a strong bias for action. We foster a supportive environment where team members grow through mentorship, innovation, and a culture of continuous learning.What you’ll be doing:
- Use business acumen and analytical skills to identify opportunities, estimate potential, layout strategy roadmaps to solve complex business problems
- Be responsible for using analytic techniques like Machine learning, Natural Language Processing and advanced data visualizations to improve Asurion’s customers’ experiences
- Experiment with latest generative LLMs to build next generative applications and boost existing ones
- Design and iterate on GenAI prompts based on performance metrics and user feedback to ensure high\-quality outputs and enhance overall system performance
- Leverage deep understanding of modern machine learning techniques and their mathematical underpinning to regularly invent new and novel approaches to solve problems
- Deliver end\-to\-end reliable and scalable AI features in production
- Provide Data Science updates to senior executive audience
- Work with stakeholders to identify new Machine Learning/AI opportunities
- Define platform and operation improvement opportunities, formulate data science problems
- Develop prototypes for new data product ideas and build data pipelines/flask apps for deployment
- Drive a Minimum Viable Product (MVP) test\-and\-learn approach and push to learn fast. Work in an iterative manner from framing problems, to building prototypes, to deploying end\-to\-end and reliable production\-grade solutions
- Help defining and monitoring the right Key Performance Indicators (KPIs) to track and deliver on critical objectives and key results
What you'll bring to the team:
- Your technical excellence in field of ML \& AI, specifically in latest Large Language Models
- Your drive to keep up with the ever\-changing AI landscape and release of new LLMs
- The ability to root cause, define, and solve complex problems in ambiguous situations. Innate curiosity and product mindset helping you articulate new ideas as well as novel technical approaches
- Self\-driven and able to ask and tackle the most important analytical questions with a view on driving product impact
- Ability to work and collaborate with different functions of the organization, from operations teammates to product managers and engineers
- Excellent communication (written and oral) and presentation skills, including creating and sharing complex ideas to peers. Ability to communicate complex quantitative analysis in a clear, precise, and actionable manner
- You’re a result\-driven thinker and doer. You offer out\-of\-the\-box ideas and aren’t afraid to roll up your sleeves to get the job done
- Respect for all people, an open mind, and an open heart. We pride ourselves on building inclusive environments. After all, it’s the diversity of thought that builds great products!
Experience and education:
- Requires a master’s degree in analytics, computer science, electrical engineering, computer engineering, or related advanced analytical \& optimization fields
- 1\-2 years of work experience in AI/ML modeling, experience in building Gen AI based products is a plus
- Solid Knowledge in Deep Learning and/or Machine Learning gained through academic coursework or any amount of internship/work experience.
- Solid experience implementing and fine\-tuning Generative AI large foundation models (LLMs), with a strong understanding of NLP techniques and frameworks such as embeddings, GPT, or Transformer models
- Knowledge of cloud computing and AI deployment (AWS, Google Cloud)
- Knowledge in Statistics, optimization theoretical concepts and/or optimization problem formulation gained through academic coursework or any amount of internship/work experience
- Solid knowledge in Python programming gained through academic coursework or any amount of internship/work experience.
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 Asurion, 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. Mid-level AI roles across all categories have a median of $165,000.
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.
Asurion AI Hiring
Asurion has 1 open AI role right now. They're hiring across Data Scientist. Based in Nashville, TN, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 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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