Machine Learning & Data Science Engineer at Tricura Insurance Group - a US- at Hire5

New York, New York, United States -

Full Time

Start Date

Immediate

Expiry Date

21 May, 26

Salary

0.0

Posted On

20 Feb, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, SQL, Regression, Classification, Gradient Boosting, Time-Series, Postgres, Snowflake, AWS, EC2, SageMaker, Git, Data Sourcing, Model Validation, API Deployment, Cloud Infrastructure

Industry

Real Estate

Description

About The Company: Tricura Insurance Group is a next-generation, tech-enabled insurance and risk-management company transforming how healthcare organizations protect, manage, and grow their operations. We’re a global remote-first team working to reshape the future of healthcare insurance. We combine deep industry expertise with cutting-edge technology to deliver smarter, faster, and more transparent insurance solutions across the continuum of care. If you're passionate about creating impactful solutions that empower those who care for others, we’d love to meet you. The Role: The title spans both disciplines because the work does. You'll analyze data and build models with sound methodology (data science), and you'll design how those models integrate into the product: architecture, deployment, cost, SLAs (ML engineering). You'll interface regularly with backend engineering on integration and with product/UX on how outputs are surfaced. The only thing not in scope is dashboards or ad-hoc business reporting. Analysis happens, but always in service of a modeling goal. The Team & Environment: Small, fully hands-on ML team with no middle management. Everyone carries their own project load. We have structured support: semiannual performance reviews, regular 1:1s, and a culture where everyone is accessible and willing to help. There's no ML platform team, no feature store, no curated datasets. You work directly with data across multiple systems. The flip side: everything you build ships to production with direct product impact. No research-only projects, no models on a shelf. The Work: Most of your time goes into figuring things out. A typical project starts with a business goal. From there you determine what data exists, where it lives, what's missing, and how to get it (internal databases, public sources, third-party providers). You define the target, build and validate the model, and deliver something the team can trust and deploy. Problems are loosely defined, data is imperfect, and dead ends happen, but nothing is wasted: if you're building it, the business needs it. For the right person, this is a growth multiplier. The breadth of problems and level of autonomy mean you won't repeat the same type of task for years. Your skills compound fast. What It Takes to Succeed: Self-management and independent judgment. In practice: End-to-end ownership. Problem framing through delivery, including sourcing data and defining the approach Rigorous self-validation. You check your outputs, investigate unexpected results, and ensure methodology is sound before work reaches review Honest communication. Clear distinction between exploration and deliverables. Early visibility into obstacles Resourceful problem-solving. Help is available and welcome. Come with what you've tried and a specific question Raising the bar. Internalize the team's quality standards and push them higher. When you find a better way, it becomes the new standard everyone builds on Responsibilities: Build and deploy predictive models for insurance pricing, risk assessment, and claims forecasting Source, clean, and engineer features from internal, external, and third-party data. Expect significant time on data work Validate rigorously: train/test splits, cross-validation, baselines, error analysis. Understand why your model works, not just that it runs Deploy to production via APIs and cloud infrastructure. Monitor and retrain as needed Communicate results to technical and non-technical stakeholders. Tie model outputs to business decisions Requirements: 1) Technical: Python and the data science / ML ecosystem Applied ML: regression, classification, gradient boosting, time-series. End-to-end (build, tune, validate, deploy) SQL fluency (Postgres, Snowflake, or similar). Comfortable with unfamiliar schemas Cloud platforms (AWS preferred: S3, EC2, SageMaker) Git 2) Equally important: Project ownership, from problem definition through delivery Self-management: own your priorities, time, and quality bar Comfort with ambiguity. Figuring things out when the path isn't clear is the norm, not the exception The instinct to question your own results before moving on Clear communication: knowing when to say "I'm exploring" vs. "I'm delivering" vs. "I'm stuck." 3) Nice to have: Healthcare, insurance, or finance background Underwriting or risk scoring experience NLP (classical or LLM-based) as a complement to traditional modeling AI-assisted coding tools (we use Claude Code a lot) What We Offer: Fully remote team across North & South American countries. Schedule: Full-time, 9 am - 6 pm EST. High-growth environment with direct exposure to leadership. Mission-driven company transforming healthcare with cutting-edge technology. Dynamic, collaborative, and fast-growing team environment. Competitive compensation, paid in USD. Unlimited PTO. Please note that the later you apply - the more intensive your selection process will be, for example, you will have fewer interview time slots to choose from, etc. Selection Process: Submit the application form with your CV Zoom screening interview with a Hire5 recruiter Technical Zoom meeting with Tricura Insurance Group team Take-home assignment (optional depending on the technical interview) Offer! Hire5 provides aspiring talents worldwide with remote opportunities to enhance their careers in Silicon Valley startups and other US-based companies. Interested in joining one of the most promising US startups? Press here to apply now or click “connect” on career.hire5.co to subscribe for future opportunities in your desired profession!

Responsibilities

Responsibilities include building and deploying predictive models for insurance pricing, risk assessment, and claims forecasting, requiring significant time spent sourcing, cleaning, and engineering features from various data sources. The role also demands rigorous validation of models and deploying them to production via APIs and cloud infrastructure.