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About This Role
Who we are
With its A.I.\-powered robotic technology platform, Symbotic is changing the way consumer goods move through the supply chain. Intelligent software orchestrates advanced robots in a high\-density, end\-to\-end system – reinventing warehouse automation for increased efficiency, speed and flexibility.
What we need
We are seeking an experienced Senior or Principal Data Scientist to lead the development of advanced simulation models that power next\-generation robotic warehouse systems. In this role, you will build high\-fidelity simulations of large\-scale robotic fleets, optimize system performance, and inform strategic product and operational decisions.
This is a highly cross\-functional position spanning data science, robotics, operations research, and distributed systems , where your work will directly impact efficiency, throughput, and scalability of real\-world automation systems.
What we do
We design, test, and deploy advanced robotic systems that improve warehouse throughput, efficiency, and reliability at scale. By reducing reliance on physical testing, we accelerate innovation cycles and drive significant operational savings and productivity gains across real\-world production environments. Our team tackles complex automation and optimization challenges alongside world\-class engineers and scientists, with the opportunity to directly influence cutting\-edge systems deployed in the field.
What y ou’ll d o
- Design and develop simulation frameworks for robotic warehouse systems, including robot fleets, inventory flows, task allocation, and human\-robot interaction.
- Build discrete\-event and agent\-based simulations to model complex, stochastic environments at scale.
- Develop predictive and prescriptive models to optimize throughput, latency, and resource utilization.
- Partner with robotics, software, and operations teams to:
- Evaluate new algorithms (routing, task assignment, scheduling).
- Test system changes before production deployment.
- Identify bottlenecks and failure modes.
- Create digital twins of warehouse environments to enable scenario testing and capacity planning.
- Apply machine learning and statistical techniques to improve simulation realism and calibration.
- Deliver clear insights and recommendations to technical and executive stakeholders.
- Establish best practices for model validation, experimentation, and reproducibility.
What you'll need
Required Qualifications
- MS or PhD in Computer Science, Data Science, Operations Research, Applied Mathematics, Physics, or related field.
- Minimum of 5 years (Senior) or minimum of 8 years (Principal) of experience in:
- Simulation, modeling, or systems optimization.
- Complex, distributed systems.
- Strong experience with:
- Discrete\-event simulation (DES) or agent\-based modeling.
- Python (NumPy, Pandas, SciPy) and/or simulation frameworks (e.g., SimPy , AnyLogic , Arena, or custom tools).
- Solid understanding of:
- Probability, stochastic processes, and statistics.
- Optimization techniques (LP, MIP, heuristics, metaheuristics).
- Experience working with large datasets and building data pipelines.
- Ability to translate real\-world system behavior into computational models.
Preferred Qualifications
- Robotics, warehouse automation, logistics , or supply chain systems.
- Fleet optimization or multi\-agent systems.
- Reinforcement learning for decision\-making.
- Path planning, task allocation, and scheduling algorithms.
- Digital twin architecture.
- Experience with C\+\+ or high\-performance systems for large\-scale simulation.
- Knowledge of cloud platforms (AWS, Azure, GCP) and distributed computing.
- Background in experimentation platforms or A/B testing in operational systems.
Principal\-Level Expectations (in addition to above)
- Define and drive the long\-term simulation and modeling strategy.
- Architect scalable simulation platforms used across the organization.
- Influence product and operational strategy through data\-driven insights.
- Mentor and grow a team of data scientists and engineers.
- Serve as a subject matter expert in simulation, optimization, and system modeling.
Tech Stack
- Python (NumPy, Pandas, SciPy, SimPy ).
- D ata platforms (Snowflake, Databricks).
- Visualization ( Grafana , Tableau).
- Cloud infrastructure (GCP).
- Optional: Python, C\# and C\+\+.
Our environment
- Up to 10% of travel may be required . Employees must have a valid driver’s license and the ability to drive and/or fly to client and other customer locations.
- The employee is responsible for owning a credit card and managing expenses personally to be reimbursed on a bi\-weekly basis.
\#LI\-JS1
\#LI\-Hybrid
About Symbotic
Symbotic is an automation technology leader reimagining the supply chain with its end\-to\-end, AI\-powered robotic and software platform. Symbotic reinvents the warehouse as a strategic asset for the world’s largest retail, wholesale, and food \& beverage companies. Applying next\-gen technology, high\-density storage and machine learning to solve today's complex distribution challenges, Symbotic enables companies to move goods with unmatched speed, agility, accuracy and efficiency. As the backbone of commerce the Symbotic platform transforms the flow of goods and the economics of supply chain for its customers. For more information, visit www.symbotic.com .
We are a community of innovators, collaborators and pioneers who embrace our differences, because we know unique perspectives make us stronger and smarter. Every perspective matters. We depend on the collective voices of our employees, customers and community to help guide us as we build a better place to work – for you and the world. That’s why we’re proud to be an equal opportunity employer.
We do not discriminate based on race, color, ethnicity, ancestry, religion, sex, national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, gender expression, veteran status, or genetic information.
The base range for this position in the posted location is $149,000\.00 \- $204,600\.00 however, base pay offered may vary depending on job\-related knowledge, skills, and experience. The compensation package includes medical, dental, vision, disability, 401K, PTO and/or other benefits.
Salary Context
This $149K-$204K range is above the median for Data Scientist roles in our dataset (median: $157K across 236 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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Symbotic, 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. This role's midpoint ($176K) sits 11% below the category median. Disclosed range: $149K to $204K.
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.
Symbotic AI Hiring
Symbotic has 2 open AI roles right now. They're hiring across Research Scientist, Data Scientist. Based in Wilmington, MA, US. Compensation range: $204K - $204K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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.
Frequently Asked Questions
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