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About This Role
The Data Scientist III plays a critical role in designing, developing, and deploying advanced data science and analytics solutions that drive actionable insights and business value. This role applies deep technical expertise and business acumen to solve complex problems, influence decision\-making, and improve operational efficiency.
Working on high\-impact and often ambiguous initiatives, the Data Scientist III independently leads significant components of projects, mentors junior team members, and proactively identifies opportunities for innovation. This role collaborates cross\-functionally with business stakeholders, engineering teams, and leadership to translate data into strategic insights and scalable solutions.
What you’ll be doing:
- Develop and implement advanced marketing measurement solutions, including Marketing Mix Modeling (MMM) and Multi\-Touch Attribution (MTA), to quantify channel performance and optimize spend
- Build and evolve customer lifetime value (CLV) models to inform acquisition, retention, and investment strategies
- Design and deploy personalization and recommendation models to enhance customer engagement and conversion across channels
- Lead experimentation strategy, including A/B and multivariate testing, to evaluate marketing initiatives and product features
- Partner with marketing stakeholders to translate analytical outputs into actionable campaign strategies and optimization plans
- Define and measure incremental impact (lift) of campaigns, promotions, and loyalty programs
- Work with large\-scale customer and behavioral datasets to create segmentation frameworks that drive targeted marketing and customer experiences
- Contribute to the development of measurement frameworks across owned, paid, and omnichannel marketing ecosystems
- Collaborate with data engineering and technology teams to ensure scalable and production\-ready solutions.
- Develop and maintain reusable data science assets, tools, and frameworks to improve efficiency and consistency.
- Present insights, recommendations, and model outputs to stakeholders, including senior leadership, in a clear and compelling manner
- Serve as a subject matter expert in selected data science domains (e.g., forecasting, customer analytics, pricing, marketing analytics, optimization)
What you bring to the table:
- Strong analytical thinking skills, with the ability to break down complex problems, identify key drivers, and translate findings into actionable insight
- Highly developed problem\-solving capabilities, with a proactive approach to identifying challenges and delivering practical, data\-driven solutions
- Collaborative mindset with a track record of working effectively across cross\-functional teams and building strong, productive relationships
- Ability to adapt quickly in a dynamic environment, balancing multiple priorities and adjusting approaches as business needs evolve
- Demonstrated initiative and ownership, consistently identifying opportunities for improvement and taking action with a sense of urgency and accountability
- Ability to balance statistical rigor with practical business impact in a fast\-paced environment
- Excellent communication skills, including the ability to clearly explain complex technical concepts to non\-technical stakeholders and tailor messaging to different audiences
What’s needed\- Basic Qualifications:
- Bachelor’s Degree in Data Science, Statistics, Computer Science, Engineering, Mathematics or a related field or equivalent work experience.
- 5\+ years of hands\-on experience building and deploying machine learning or statistical models in a business environment
- Strong understanding of statistical methods, including hypothesis testing, regression, and model evaluation techniques
- Experience with marketing analytics use cases such as attribution, campaign measurement, personalization, or customer segmentation
- Hands\-on experience with experimentation design and analysis (e.g., A/B testing, uplift modeling)
- Proficiency in Python or R, with demonstrated experience using libraries such as pandas, scikit\-learn, TensorFlow, PyTorch, or equivalent
- Experience working with large datasets using SQL (e.g., writing complex queries, optimizing performance)
- Demonstrated ability to independently execute end\-to\-end data science projects (minimum of 3 completed projects with measurable business impact)
- Experience communicating technical results to non\-technical stakeholders (e.g., presentations, dashboards, reports)
What’s needed\- Preferred Qualifications:
- Master’s or PhD in Data Science, Statistics, Computer Science, or a related field
- 6\+ years of experience in advanced analytics, machine learning, or AI
- Experience deploying models into production environments (e.g., APIs, cloud platforms such as AWS, Azure, or GCP)
- Proficiency with big data technologies (e.g., Spark, Hadoop)
- Experience with MLOps practices, including model monitoring, versioning, and lifecycle management
- Domain expertise in areas such as retail, e\-commerce, pricing, supply chain, or customer analytics
- Experience leading project workstreams or mentoring junior team members
- Demonstrated ability to deliver solutions that resulted in measurable business outcomes (e.g., % revenue uplift, cost savings, efficiency gains)
- Experience building and deploying Marketing Mix Models (MMM) or Multi\-Touch Attribution (MTA) solutions
- Experience working with customer lifetime value (CLV) modeling and lifecycle analytics
- Experience developing personalization or recommendation systems in a marketing or e\-commerce context
- Familiarity with causal inference techniques and incrementality measurement frameworks
- Experience supporting marketing organizations (e.g., paid media, CRM, loyalty, digital analytics)
We Offer:
- Inclusive culture with associate\-led Business Resource Groups
- 22 days of PTO and Holiday Schedule (7 observed paid holidays \+ 1 floating holiday)
- Online and Retail Discounts, Company Match 401(k), Physical and Mental Health Wellness programs, and more!
The salary range represents the expected compensation for this role at the time of posting. The specific base pay may be influenced by a variety of factors to include the candidate's experience, skill set, education, geography, business considerations, and internal equity. In addition to base pay, this role may be eligible for bonuses, or other forms of variable compensation.
Salary Context
This $98K-$136K range is in the lower quartile for Data Scientist roles in our dataset (median: $162K across 211 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 3,824 AI roles we're tracking, Data Scientist positions make up 7% of the market. At Staples, 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 $200,000 based on 697 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($117K) sits 42% below the category median. Disclosed range: $98K to $136K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Staples AI Hiring
Staples has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Framingham, MA, US. Compensation range: $136K - $229K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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