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About This Role
### About Veho
Veho’s mission is to power the future of commerce by making shopping, shipping and returns seamless for everyone.
We are building a modern, end\-to\-end logistics infrastructure designed entirely for the ever\-evolving needs of ecommerce brands and everyday consumers.
Powered by next\-generation technology and a vertically integrated supply chain, Veho gives brands and their customers unprecedented control over their deliveries and removes the pain from the ecommerce post\-purchase experience. We make delivery the ‘extension of the brand’ and leverage it to create deeper loyalty and trust between brands and their customers, driving customer retention and lifetime value. Our rapidly growing client list includes leading consumer brands like Hello Fresh, Zara, Macy’s, Sephora, and more.
To truly build an iconic company, we strongly believe that our people and values must be aligned with our mission. As such, we take pride in our championship team, merit\-based culture. We seek team players who want to compete, win, make an impact and build a legacy, and we reward performance and impact players with generous equity and incredible career growth opportunities.
About The Role:
As a Staff Data Scientist for Forecasting, you will be the technical leader responsible for building and owning the sophisticated models that power Veho's Sales and Operations Planning (S\&OP) process. Your forecasts will be the backbone of our strategic and operational decision\-making across our network: it ensures we understand what volume we should expect in our network, how it will flow from injection to delivery, and where our bottlenecks will be.
You’ll be directly embedded in a team of talented software and data engineers and partner closely with leaders in Operations, Finance, and Product to translate their business challenges into scalable forecasting solutions. You will be responsible for operationalizing the models you build in the production systems that drive Veho's growth and efficiency.
What you’ll do:
- Create the forecasting algorithms that drive our Sales \&Operations Planning process.
- Building reliable, efficient, and scalable models for our AI/ML capabilities
- Creating robust data pipelines to feed analyses and models
- Analyze and evaluate the impact and effectiveness of models in production systems
- Driving science roadmap for forecasting for Veho alongside cross\-functional partners.
- Leverage AI\-native development workflows and tooling (Claude Code, Cursor, Copilot, etc.) to accelerate throughput
- Mentor other Data Scientists and drive team\-wide improvements to our data science practices.
What You Bring:
- Bachelor’s Degree plus 8 years of experience in Data Science, Data Engineering or a similar degree, or Master’s Degree plus 5 years of experience, or Phd plus 3 years of experience
- Deep expertise in a variety of time series forecasting methods (e.g., ARIMA, Prophet, exponential smoothing, LSTMs, Transformer\-based models) and a strong understanding of their theoretical underpinnings.
- Experience in developing and testing forecasting systems in a supply chain context, combining statistical forecasting with human inputs. Experience building and operationalizing forecasting models for S\&OP, demand planning, inventory management, or similar business processes.
- Experience leading complex, end\-to\-end machine learning and data science projects from conception to production.
- Experience developing both greenfield systems and building on top of existing systems.
- Experience leading projects in collaboration with Operations, Product, Engineering teams.
- Strong ability to use AI to accelerate model iteration and testing
- Strong Python and SQL skills, to develop production applications
- Experience with MLOps and model deployment pipelines
- Experience in supply chain is a strong plus.
- Experience with combinatorial optimization methods is nice to have but not required
Veho is a growth company that looks for team members to grow with it. No matter the location, or the role, every Veho teammate shares one galvanizing mission: driving commerce forward with a customer\-centric delivery and returns experience that’s built for the modern era. We are deeply value\-driven (Team Up, Drive Impact, Take Ownership, Solve Bigger, Obsess Over Experience, Make Today Count) and care tremendously about investing in our high\-performers.
Join us in building the future of ecommerce logistics and in doing the work of our lifetime!
All California applicants please reference our California Applicant Privacy Notice located here.
Compensation Range: $185K \- $220K
Salary Context
This $185K-$220K range is above the 75th percentile for Data Scientist roles in our dataset (median: $155K across 226 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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Veho, 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 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($202K) sits 5% above the category median. Disclosed range: $185K to $220K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Veho AI Hiring
Veho has 7 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, MLOps Engineer. Positions span New York, NY, US, US. Compensation range: $200K - $325K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
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