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.
Skills Required
- Bachelor's in CS, Data Science, Biostatistics, Mathematics, or similar AND 8+ years of professional data science experience (OR Master’s/PhD AND 5+ years)
- Expert-level knowledge of deep learning and machine learning algorithms
- Proficiency in Python and core Python data science ecosystem
- Experience with natural language, medical data, long-tail predictions, or similar problem spaces
- Strong familiarity with all phases of the MLOps model lifecycle and creating team standards
- Accountability for model impact long-term including monitoring for drift, KPI/impact monitoring, and triaging issues
- Strong communication and collaboration skills with nontechnical stakeholders and leadership
- Experience with time series and sequence modeling and working with big, unstructured data
- Experience with GLMs, XGBoost, Transformers, and hybrid modeling approaches
- Fluency with actuarial methods and familiarity with underwriting/claims or healthcare/medical data
What the Team is Saying
Gradient AI Compensation & Benefits Highlights
-
Healthcare Strength — Health coverage is presented as comprehensive, including medical, dental, vision, HSA/FSA options, company‑paid life/AD&D, and short/long‑term disability, with access to an Employee Assistance Program. Materials also indicate coverage can begin immediately and plans are administered via a broad PEO network.
-
Leave & Time Off Breadth — Time off provisions emphasize unlimited PTO, 11 paid holidays, and periodic companywide “Recharge Days.” These elements are positioned to support rest and work‑life balance beyond standard vacation policies.
-
Equity Value & Accessibility — Equity grants are described as available to all new hires with a four‑year vesting schedule. Performance bonuses are also noted alongside equity as part of total rewards.
Gradient AI Insights
What We Do
Gradient AI is revolutionizing Group Health and P&C insurance with AI-powered solutions that help insurers predict risk more accurately, improve profitability, and automate underwriting and claims. Our SaaS platform taps into one of the industry’s largest data lakes—tens of millions of policies and claims—to deliver deep, actionable insights. Trusted by leading carriers, MGAs, TPAs, and self-insured employers, Gradient AI has grown rapidly since our founding in 2018. Backed by $56M in Series C funding, we're scaling fast—and it's an exciting time to join the team.
Why Work With Us
Gradient is a fast-growing startup where you can make an impact, learn constantly, and grow quickly. We’re bringing AI and machine learning into the insurance space in new ways, and every team member plays a role in that innovation.
Gallery
Gradient AI Offices
Hybrid Workspace
Employees engage in a combination of remote and on-site work.
Working from the office is optional if you are local to our Boston office. For employees who do come in, it’s usually T/W/Th to focus on group collaboration.
