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About This Role
At Tive, we imagine a fully\-transparent world in which everything and everyone is connected. We innovate beyond what the world thought possible—so what people hold near and dear arrives on time and in full.
We believe (and live!) four core values:
- Transparency First: We make everything visible so that everyone can operate better
- Always strive to make things simpler
- We are One Team, we have each other's backs
- Relentless iteration to optimize and improve
Whether shipped by road, rail, ocean, or air, Tive reduces shipment delays, minimizes rejected loads, and decreases theft, damage, and spoilage. Our customers count on Tive to ensure that their shipments are delivered on time and in full—because every shipment matters.
As a Senior Data Scientist at Tive, you will own and drive data science and machine learning features end\-to\-end: from problem framing, solutioning, and modeling through to production and monitoring. You will work at the intersection of AI, machine learning, geospatial intelligence, and real\-world logistics data to build capabilities that turn raw signals into intelligence customers can act on. The best ideas only count if they ship.
The problems we work on don't have established playbooks. Our trackers generate a rich proprietary dataset of real\-world shipment, sensor, and geospatial data, and we own it end to end, from device to platform. We work within a rich space of possibilities for delivering value to our customers.
What Our Ideal Candidate Will Do:
- Own and ship innovative customer\-facing data science and AI features across Tive's core roadmap spanning theft detection, routing intelligence, anomaly detection, and alerting.
- Drive initiatives end\-to\-end from ideation, scoping, modeling, to production and deployment, with full accountability for outcomes and execution.
- Bring creativity to unsolved problems where the right approach isn't obvious and the playbook doesn't exist yet.
- Build with production in mind from day one. Evaluation harnesses, precision/recall tracking, shadow\-mode testing, and clean handoffs to platform engineering are how we work.
- Collaborate closely with product managers, platform engineers, and other stakeholders within a structured, outcome\-oriented process.
- Hold a high bar for quality, transparency, and rigor.
Preferred Experience:
- 5\+ years of industry data science experience, with a track record of owning initiatives end\-to\-end from proof of concept to production.
- Master's, PhD, or equivalent work experience in computer science, mathematics, statistics, engineering, or a related technical field.
- Senior\-level depth in statistical modeling, ML, and modern AI/LLM techniques
- Experience in owning ambiguous, cross\-functional problems from definition through execution.
- Strong communication skills: you can translate complex modeling decisions into clear business context for product and executive stakeholders.
- Geospatial, supply chain, or IoT sensor data experience is a strong plus: our data is inherently spatial and physical, and domain intuition accelerates impact.
- Strong Python skills and solid data engineering fundamentals.
- Proficiency in SQL. Experience with dbt is a plus.
- Hands\-on experience deploying and operating ML in Snowflake, AWS, or comparable cloud infrastructure.
- Familiarity with Agile/Scrum development methodologies.
What does Tive offer?
- A chance to join what may very well turn out to be the most important company in your career.
- The autonomy and resources to build what you know how to build.
- Work with a committed global team that have each others back.
- Office\-based or hybrid options. Your choice.
- Competitive equity to ensure all of our employees have a sense of ownership in the long\-term success of Tive’s growth.
*We celebrate diversity, and consider it key to our success as both a team and a company. We are proud to be an equal\-opportunity employer, and we are committed to creating an inclusive environment of mutual respect for all employees.*
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 Tive, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
Tive AI Hiring
Tive has 1 open AI role right now. They're hiring across Data Scientist. Based in Boston, MA, US.
Location Context
AI roles in Boston pay a median of $215,350 across 442 tracked positions. That's 8% above the national 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.
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