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About This Role
About
Bloomingdale’s makes fashion personal and fun, aspirational yet approachable. Our mission is to guide and inspire our customers to make style a source of creative energy in their lives. We will always strive to make Bloomingdale’s like no other store in the world. Across all brand touchpoints—from Bloomingdales.com to our newest small store concept, Bloomie’s—everyone plays a critical role bringing our mission to life. Our inclusive culture promotes diversity of background, thought and opinion. Regardless of position, we believe all colleagues have a voice and access to share their thoughts with every level of leadership. Our colleagues are passionate, driven, entrepreneurial and collaborative, while having a lot of fun along the way.
Job Overview
The Lead, Data Scientist will design, develop, and implement advanced data science models for priority business use cases across the enterprise, such as online experience, marketing, merchandising, supply chain, finance, and store operations. The Lead Data Scientist will work in the areas of feature engineering, deep learning, data insights and analytics. The Lead Data Scientist will build new AI enabled smart services that surprise and delight our customers and work with big data (text, images, audio \& other) to solve real\-world problems.
Essential Functions
- Thought leader in data science and analytics who can help the business define their business problem, create solutions to address it, plan and execute the implementation.
- Collaborate with business stakeholders to define business requirements including KPI and acceptance criteria.
- Lead research initiatives into state\-of\-the\-art methodologies that will enhance current models and power future personalized models.
- Collaborate with data engineers, ML engineers and Data Scientists in building real\-time and batch machine learning pipelines that include data preprocessing, feature engineering, model training, model validation, serving, and evaluating results of A/B test.
- Leadership: Promotes efficacy through monitoring, coaching \& motivating teammates
Qualifications and Competencies
- Master’s degree required, Ph. D. preferred; engineering, business, finance, data science, computer science, or other relevant fields of study encouraged.
- SQL and Python experience.
- Blend of applied math, data science and engineering skills, proven track record of solving critical business problems through data science and strong analytical/quantitative and engineering skills.
- Ability to think creatively, strategically, and technically to identify data issues and take actions to remediate.
- Familiarity with visualization tools such as Tableau, Power BI or Looker.
- Experience with web reporting tools like Adobe Analytics or Google Analytics.
- Ability to conduct qualitative market research and perform qualitative analysis, identify trends and patterns in the data and tell a compelling story through visualization and presentation.
- Experience in leveraging data cleaning, transformation, analytics and reporting from large data sets to support use cases and drive business value.
- Experience with Cloud (GCP, Azure, AWS) and big data processing technologies (Redshift, Big Query) is a plus.
- Ability to collaborate and work closely with cross\-functional teams \- Data Scientists, Data Engineers, Data Analysts, business and solution architects and partners.
- Demonstrated leadership skills.
- Excellent communication skills, especially when it comes to requirements gathering, communicating with stakeholders, and summarizing and presenting analysis.
- This role involves performing essential job functions such as communication, collaboration, and use of office and computer systems. Responsibilities include the ability to access and review written and electronic information, and to move within the work environment and interact with workplace materials as needed to carry out job responsibilities.
*This job description is not all inclusive; additionally, Macy’s, Inc. reserves the right to amend this job description at any time. Macy’s, Inc. – including Macy’s, Bloomingdale’s, and Bluemercury – is an equal opportunity employer, committed to a diverse and inclusive work environment.*
This position may be eligible for performance\-based incentives/bonuses. Benefits include 401k, medical/vision/dental/life/disability insurance options, PTO accruals, Holidays, and more. Eligibility requirements may apply based on location, job level, classification, and length of employment. Additional benefit details are available at bloomingdalesJOBS.com.
Salary Context
This $155K-$259K 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 Bloomingdale's, 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 ($207K) sits 8% above the category median. Disclosed range: $155K to $259K.
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.
Bloomingdale's AI Hiring
Bloomingdale's has 1 open AI role right now. They're hiring across Data Scientist. Based in Long Island City, NY, US. Compensation range: $259K - $259K.
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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