Interested in this Data Scientist role at Gradient AI?
Apply Now →Skills & Technologies
About This Role
*This is a fully remote opportunity with hybrid available to those local to Boston.*
Gradient AI:
Gradient AI is the decision\-intelligence partner for the insurance industry, giving customers an advantage in how they make decisions by revealing risk others miss and translating it into stronger performance and real\-world impact. Our platform harnesses a vast industry data lake – tens of millions of policies and claims enriched with economic, health, geographic, and demographic signals – integrating cleanly with existing workflows to make complex risk clear, usable, and actionable. Our customers include carriers, brokers, consultants, and specialized insurance organizations across the industry. We are backed by $56M in Series C funding and scaling fast – and it's an exciting time to join the team!
About the Role:
We are looking for a Principal Data Scientist with deep, specialized expertise to lead our organization's most complex and high\-impact modelling and analytical initiatives. As a recognized technical authority, you will set modelling strategy across the organization, drive our most novel work, and raise the technical standard for how we build and ship models.
How you will make an impact:
- Leverage the best of modern deep learning \& large language models with traditional data science techniques to create powerful hybrid models with real uplift.
- Brainstorm, prototype, prove, deploy, and realize the value of your work in market quickly.
- Everything you would expect on a world\-class data science team solving world\-class problems. Big data. Federated learning. Unstructured data challenges. Timeseries and sequence modelling. A self\-serve buffet of techniques from GLMs to XGBoost to Transformers.
- Tell stories with your data. Inspire trust in customers, stakeholders, and prospects by turning murky math into a powerful message that drives the bottom line.
Who you are and why we want to work with you:
- You like getting things over the line. You have an insatiable desire to deliver value now and improve next. MVP perfection is achieved not when there is nothing more to add, but when there is nothing left to take away.
- You are not a software engineer, but you give them a run for their money. You prefer Python to R and don't understand why there is still a debate. Jupyter is a necessary evil, and you've never met a command line that scared you away.
- You still do a better job than Claude, and you're skeptical of your friends who say they never code any more.
- You love to take initiative and spearhead new projects, even if they are not well defined.
- You build systems bigger than you. You contribute to open source, build packages your peers want to use, or design frameworks to elevate your team. Reuse is a strategy, not a buzzword.
Skills needed to succeed:
- Bachelor's degree in Computer Science, Data Science, Biostatistics, Mathematics, or similar related field AND 8\+ years of professional data science experience building predictive models
- OR Master's or Ph.D in Computer Science, Data Science, Biostatistics, Mathematics, or similar related field AND 5\+ years of professional data science experience building predictive models
- Expert\-level knowledge of deep learning and ML algorithms and the core Python data science ecosystem.
- Strong communication and collaboration skills, particularly communicating with nontechnical stakeholders and leadership, and helping to pivot technical roadmaps to deliver their intended value rapidly
- Deep experience with natural language, medical data, long\-tail predictions, or similar related problem spaces
- Strong familiarity with all phases of the MLOps model lifecycle, with experience creating team standards and practices to enforce quality and speed
- Deep experience being accountable for model impact long term – from MLOps pipelines to monitor for drift, to KPI and impact monitoring, driving incremental and long\-term improvements, triaging issues, addressing tech debt responsibly, etc.
Bonus Qualifications:
- Fluency with actuarial methods and working with actuaries is a plus
- Familiarity with healthcare and medical data
- Familiarity with underwriting and claims, or predicting long\-tailed and/or rare events
What We Offer:
- A fun, team\-oriented startup culture.
- Generous stock options \- we all get to own a piece of what we're building.
- Unlimited vacation days.
- Flexible schedule that supports working from home.
- Full benefits package includes medical, dental, vision, 401k, paid paternal leave, and more.
- Ample opportunities to learn and take on new responsibilities.
We are an equal opportunity employer.
Salary Range: $190,000\-235,000k base salary annually.
This role is also eligible for an annual performance bonus, equity grant, and a comprehensive benefits package. In accordance with the Massachusetts Pay Transparency Law, we are providing a good\-faith salary range for this position at the time of posting. The actual salary offered will depend on the level at which the candidate is hired, as well as their experience, skills, qualifications, and location. Compensation may grow over time through merit\-based increases, promotions, and company\-wide adjustments. If your salary expectations fall outside this range, we still encourage you to apply so we can have a conversation.
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 Gradient AI, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
Gradient AI AI Hiring
Gradient AI has 3 open AI roles right now. They're hiring across Data Scientist. Based in Boston, MA, US.
Location Context
AI roles in Boston pay a median of $210,000 across 166 tracked positions.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.