Interested in this Data Scientist role at Traba?
Apply Now →Skills & Technologies
About This Role
Traba is the AI operating layer for the industrial supply chain. We started in workforce—temp staffing, the biggest operational pain point for the manufacturing and logistics customers we serve—and used it to embed ourselves inside their daily operations and create a far better customer experience through technology. Now those same customers are pulling us beyond staffing into the broader operational workflows that run their facilities. That foundation gave us proprietary data from millions of shifts and deep enterprise relationships. But our edge is more than data: by connecting to the systems running across every facility and activating the workers already on our platform to execute against them, we are building applied AI that drives real productivity gains and transforms how the global supply chain operates at scale.
We are backed by Founders Fund, Khosla Ventures, and General Catalyst.
Traba is hiring a Senior Data Scientist to join the founding Agents team and lead measurement and modeling for our agentic platform from 0 1\. You’ll make the core calls on how agent quality is defined, measured, and improved; set the bar for statistical and scientific rigor; and build the evaluation, experimentation, and modeling foundations that every agent we ship is measured against.
As a Senior Data Scientist at Traba, you’ll own how we model and understand agent performance inside real customer workflows—capability, reliability, and unit economics—and partner with engineering, product, and operations leadership on the decisions that shape the platform.
Responsibilities:
- Provide strategic insights and recommendations to senior leadership through in\-depth statistical analysis and modeling.
- Design, build, and maintain the metrics, models, and reporting that track agent quality, reliability, adoption, and unit economics for stakeholders across the Agents and Operations teams.
- Build evaluation and experimentation as a first\-class discipline—datasets from production traces, rubrics, automated graders, regression suites, and the experiment design and analysis that prove causation—so every agent improvement we ship is backed by evidence.
- Identify key business challenges and opportunities—including agent failure modes, tool\-use patterns, and cost and latency—and build statistical and machine\-learning models to drive product improvements and growth initiatives.
- Architect scalable analytics and modeling infrastructure to ensure data integrity, governance, and accessibility for both human and agent consumers.
- Oversee the development and maintenance of Traba’s data warehouse to ensure data availability and governance.
- Work closely with the Agents team and Operations leadership to understand their data needs and provide actionable, statistically grounded insights that drive continuous process improvement and operational efficiency.
- Provide Operations teams with models, self\-service analytics, and advanced technologies—including AI\-assisted tools—enabling them to independently analyze operational data and optimize their daily activities.
- Mentor the scientists and analysts who build alongside you, and set the standards that define what “good” looks like for measurement, modeling, and experimentation at Traba.
Qualifications:
- Experience: 4\-8 years in data science, machine learning, applied statistics, or quantitative research, with 2\+ years of hands\-on work modeling or measuring LLM\- or agent\-based systems in production.
- Education: BS/MS/PhD in data science, statistics, machine learning, computer science, mathematics, economics, or a related quantitative field (or equivalent work experience).
- Technical Skills:
+ Strong proficiency in Python and common ML and statistics libraries (e.g., scikit\-learn, PyTorch, pandas, statsmodels).
+ Strong proficiency in SQL.
+ Experience designing and analyzing experiments (A/B testing) and applying statistical inference or causal methods.
+ Experience with LLM evaluation and observability tools like Langfuse, Braintrust, or internal harnesses, and with building automated evaluators.
- Communication Skills: Excellent data storytelling skills to effectively engage with stakeholders.
- Collaboration Skills: Strong ability to work across departments, identifying and prioritizing analytics problems to deliver actionable insights.
- Curiosity and Initiative: Intense curiosity to ask “why?” and use data to find answers, combined with a “no task too small” mentality.
- Self\-Motivation: Ability to work independently and as part of a team in a fast\-paced startup environment.
Bonus Skills:
- Experience with notebook tools like Jupyter, Hex, Hyperquery, or equivalent.
- Experience with modern data stack tools like dbt or equivalent.
- Experience building internal agents or MCP servers for analytics workflows, or prior work at a vertical AI or AI\-native data company (e.g., Hex, Omni, dbt).
- Experience fine\-tuning, distilling, or rigorously evaluating LLMs, or applying causal inference and experimentation at scale.
Benefits:
- Start\-up equity
- Competitive Salary
- 100% Paid health, dental \& vision coverage
- Dinner Provided via DoorDash, free DashPass \& stocked kitchen for NY employees
- Commuter benefit
- Gympass Benefit
- ✚✚ Additional: One Medical Membership, Gympass, HSA via Optum, Talkspace, HealthAdvocate, Teledoc Health
Salary Range Details
------------------------
The compensation range for this position is set between $180,000 and $215,000, reflecting our market analysis and other relevant considerations. However, exceptions may be made for candidates with qualifications that significantly differ from those outlined in the job description. We also offer a highly competitive equity package designed to ensure you share meaningfully in the long\-term success and upside of the business.
The position is based onsite in New York City, five days per week, giving you the opportunity to collaborate closely with a high\-performing team in a fast\-paced, energetic environment. Being in person supports real\-time decision\-making, stronger teamwork, and the kind of creative problem\-solving that drives meaningful impact.
Our Values
--------------
Dream BIG \- We are on a path to change the world for the better. We create and communicate a bold direction that inspires a life\-changing vision. We don’t sacrifice long\-term value for short\-term results.
Olympian’s Work Ethic \- Changing the world never comes easy. We work harder, longer, and smarter, not just two out of three. We put everything we have on the field.
Growth Mindset \- We confront the toughest challenges head\-on and persevere. Sometimes we fail, but we brush ourselves off, adapt, learn, and push forward with resilience.
Customer Obsession \- We go the extra mile for our workers and businesses. We remain focused on delivering high\-quality products and services that solve these often overlooked communities’ problems.
What is Light Industrial Labor?
-----------------------------------
Light industrial labor drives the efficiency of global supply chains, encompassing essential, entry\-level roles in warehouses and distribution centers. These workers pack boxes, load trucks, and manage day\-to\-day operations that ensure goods move seamlessly to meet growing consumer and business demands. It’s a $200B\+ global market and a critical part of keeping goods moving smoothly in today's economy.
Job Applicant Privacy Notice
Compensation Range: $180K \- $215K
Salary Context
This $180K-$215K range is above the 75th percentile for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →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 Traba, 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. Disclosed range: $180K to $215K.
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.
Traba AI Hiring
Traba has 1 open AI role right now. They're hiring across Data Scientist. Based in New York, NY, US. Compensation range: $215K - $215K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 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.