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About This Role
About the Role
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Total Wine \& More is seeking a Senior Data Scientist, Operations Research to join our Data Services team in Bethesda, MD. In this role, you will lead the design and deployment of optimization and decision science solutions that directly drive business outcomes across Total Wine \& More. This role focuses on solving complex, large\-scale mathematical problems to inform strategic and operational decisions, including pricing, inventory, supply chain, and marketing optimization. You will work at the intersection of data, mathematical modeling, and decisioning systems, translating business problems into formal optimization frameworks and delivering production ready solutions. This role requires deep hands\-on experience in operations research (OR), including building and solving large\-scale optimization models, alongside strong data engineering and software skills to operationalize those solutions. This role will report to the Sr. Director, Data Science.
You will
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- Formulate decision problems as mathematical optimization models (LP, MILP, MINLP), translating business constraints and objectives into structured frameworks
- Design and implement large\-scale optimization models, solving complex problems involving thousands to millions of variables and constraints
- Leverage solvers such as Gurobi (preferred) or equivalent tools to build high\-performance solutions, including model tuning and performance optimization
- Develop end\-to\-end decision systems, integrating optimization outputs into production workflows and business processes
- Collaborate with business stakeholders to define objective functions, constraints, and trade\-offs, ensuring solutions align with real\-world decision\-making
- Evaluate solution quality through scenario testing, sensitivity analysis, and stress testing of optimization results
- Scale and productionize models, including API development, automation, monitoring, and retraining workflows
- Build and maintain data pipelines to support optimization models, ensuring data quality, availability, and performance at scale
- Communicate model structure, assumptions, and results clearly to leadership, translating complex math into actionable business insights
You will come with
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- Bachelor's Degree in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, or related field or equivalent years of experience
- Master's Degree in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, or related field or equivalent years of experience
- 3\-6 years’ experience in Operations Research / Optimization or related fields preferred
- Hands\-on experience with Gurobi (strongly preferred) or similar solvers (CPLEX, OR\-Tools, etc.)
- Proven experience building and deploying large\-scale mathematical models (LP, MILP, etc.) in production environments
- Ability to design and maintain data pipelines for large\-scale modeling workflows and experience working with large, complex datasets, including feature engineering and data validation
- Proficiency in Python and SQL, with emphasis on scalable, maintainable code
- Familiarity with cloud environments (AWS, Azure, GCP) for scalable execution
We offer
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- Paid Time Off (PTO)
- Generous store discounts
- Health care plans (medical, prescription, dental, vision)
- 401(k), HSA, FSA, Pre\-tax commuter benefits
- Disability \& life insurance coverage
- Paid parental leave
- Pet insurance
- Critical illness and accident insurance
- Discounted home and auto insurance
- College tuition assistance
- Career development \& product training
- Consumer classes
- *\& More!*
Crafted for you
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We recognize our team members are our biggest asset, and we value the critical role each play in contributing to the company’s success. It is our commitment to support and provide access to the resources needed to take care of their health and wellbeing. That is why we offer a variety of benefits, tools, and resources to support through our Total Rewards program. To view our full career page, click here: https://careers.totalwine.com/!
*Total Wine \& More considers several factors when establishing compensation. Compensation may vary based on a number of factors including, but not limited to, market location, job\-related knowledge, skills and/or experience. The actual hourly rate will equal or exceed the required minimum wage applicable to the job location. Estimated salaries determined by third parties have not been validated by Total Wine \& More.*
*Total Wine \& More is an equal opportunity employer and all qualified applicants will receive consideration for employment without discrimination based on race, color, religion, national origin, sex, sexual orientation, age, marital status, veteran status, disability, or any other characteristic protected by applicable law. Total Wine \& More makes reasonable accommodations during all aspects of the employment process, including during the interview process. Total Wine \& More is a Drug Free Workplace.*
*The information provided above indicates the general nature and level of work required of the position and is not a comprehensive list of all responsibilities or qualifications. Benefits list is only a highlight of some of the benefits offered to team members; eligibility for certain benefits apply.* *The anticipated close date of this posting is 120 days from the posted date indicated above.*
Worker Type:
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RegularPay Range:
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$122,200\.00 \- $165,000\.00 Annual
Salary Context
This $122K-$165K range is below the median 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 Total Wine & More, 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. This role's midpoint ($143K) sits 26% below the category median. Disclosed range: $122K to $165K.
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.
Total Wine & More AI Hiring
Total Wine & More has 1 open AI role right now. They're hiring across Data Scientist. Based in Bethesda, MD, US. Compensation range: $165K - $165K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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
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