Interested in this Data Scientist role at Timberland?
Apply Now →Skills & Technologies
About This Role
Senior Data Scientist
Let's Talk About the Role
VF Corporation is seeking an exceptional Senior Data Scientist to help accelerate our Data & AI transformation. This role combines advanced data science, machine learning, and Generative AI expertise with the opportunity to solve complex business challenges across some of the world's most iconic apparel and footwear brands.
As a Senior Data Scientist, you will partner with business, product, engineering, and technology teams to design, develop, and deploy scalable AI and machine learning solutions that deliver measurable business value. From demand forecasting and marketing optimization to Generative AI applications and intelligent automation, you'll translate ambiguous business opportunities into production-ready solutions that influence decision-making across VF.
This is an opportunity to work at the forefront of Applied AI, helping shape how VF leverages machine learning, large language models, and modern AI technologies to deliver innovative experiences for consumers and associates worldwide.
What You Will Do
Driving Business Impact Through Data Science
- Develop advanced machine learning solutions that improve decision-making across marketing, merchandising, supply chain, planning, and digital commerce.
- Design and implement predictive models, forecasting solutions, and optimization algorithms to solve complex business problems.
- Translate business challenges into analytical approaches that drive measurable outcomes and ROI.
- Partner with stakeholders to identify opportunities where AI and advanced analytics can create competitive advantage.
Build and Scale AI Solutions
- Design, develop, and produce machine learning and Generative AI applications.
- Build and deploy solutions powered by large language models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and multimodal AI capabilities.
- Evaluate, fine-tune, and optimize AI models using both proprietary and open-source frameworks.
- Ensure AI solutions are scalable, reliable, secure, and aligned with responsible AI principles.
Lead End-to-End Model Development
- Partner with Data Engineers to acquire, prepare, and manage large-scale data sets.
- Develop and validate statistical, machine learning, deep learning, and forecasting models.
- Monitor model performance and continuously improve solutions through experimentation and optimization.
- Establish best practices for model development, testing, deployment, and lifecycle management.
Influence Across Functions
- Present technical findings and business recommendations to leadership and non-technical stakeholders.
- Collaborate with Product Managers, Data Engineers, DevOps teams, UX designers, Legal, and Operations partners.
- Advocate for data-driven decision making and support broader adoption of AI solutions across the enterprise.
- Serve as a trusted technical expert and thought partner on Data Science and AI initiatives.
Skills for Success
We are looking for a highly skilled data scientist who combines deep technical expertise with strong business acumen and stakeholder leadership.
Data Science & Machine Learning
- Expertise in machine learning techniques including regression, classification, clustering, recommendation systems, forecasting, and optimization.
- Strong foundation in statistics, hypothesis testing, experimental design, and predictive analytics.
- Experience developing and deploying production-ready machine learning models.
- Advanced knowledge of time-series forecasting methodologies and model evaluation techniques.
Generative AI & AI Engineering
- Experience designing and deploying enterprise Generative AI solutions.
- Proficiency working with large language models, prompt engineering, model evaluation, and fine-tuning techniques.
- Experience building solutions using RAG architectures, vector databases, embeddings, and knowledge retrieval systems.
- Experience developing AI agents, autonomous workflows, and orchestration frameworks.
- Understanding of responsible AI practices, governance, explainability, privacy, and risk management.
Programming & Data Platforms
- Advanced Python development skills with expertise in data science and machine learning libraries.
- Strong SQL and data engineering capabilities supporting large-scale analytics environments.
- Experience using cloud-based data and AI platforms, preferably Azure Databricks.
- Familiarity with distributed data processing, MLOps practices, CI/CD pipelines, and model lifecycle management.
Leadership & Communication
- Ability to translate complex technical concepts into actionable business insights.
- Proven ability to influence decisions across cross-functional teams and leadership groups.
- Strong problem-solving skills and ability to work effectively in ambiguous environments.
- Collaborative mindset with a passion for mentoring, knowledge sharing, and continuous learning.
Preferred Technologies
Experience with many of the following technologies is preferred:
- Python, Pandas, NumPy, Scikit-Learn, PyTorch, TensorFlow
- Azure Databricks, Delta Lake, MLflow, Unity Catalog
- LangChain, LangGraph, OpenAI APIs, Model Context Protocol (MCP)
- Vector databases and RAG architectures
- Azure DevOps, Git, CI/CD workflows
- Streamlit or similar application frameworks
Education & Experience
- Master's degree or Ph.D. in Data Science, Computer Science, Machine Learning, Applied Mathematics, Engineering, or a related field.
- Typically 5+ years of progressive experience developing and deploying advanced analytics, machine learning, or AI solutions.
- Experience delivering solutions in retail, consumer products, apparel, eCommerce, or consulting environments is a plus.
- Proven record of successfully translating business problems into scalable, production-ready AI and data science solutions.
Hiring Range:
$142,560.00 USD - $178,200.00 USD annuallyIncentive Potential: This position is eligible for additional compensation awards that may include an annual incentive plan, sales incentive, or commission potential. Specific details of the additional compensation eligibility for this position will be provided during the recruiting and interview process.
Benefits at VF Corporation: You can review a general overview of each benefit program offered, including this year's medical plan rates on www.MyVFbenefits.com and by clicking Looking to Join VF? Detailed information on your benefits will be provided during the hiring process.
P**lease note, our hiring ranges are determined and built from market pay data. In determining the specific compensation for this position, we comply with all local, state, and federal laws.
At VF, we value a diverse, inclusive workforce and we provide equal employment opportunity for all applicants and employees. All qualified applicants for employment will be considered without regard to an individual’s race, color, sex, gender identity, gender expression, religion, age, national origin or ancestry, citizenship, physical or mental disability, medical condition, family care status, marital status, domestic partner status, sexual orientation, genetic information, military or veteran status, or any other basis protected by federal, state or local laws. If you require accommodations during the application process, please contact us at [email protected]*. VF will provide reasonable accommodations for qualified individuals to the extent required by applicable law.*
Pursuant to all applicable local Fair Chance Ordinance requirements, including but not limited to the San Francisco Fair Chance Ordinance, VF will consider for employment qualified applicants with arrest and conviction records.
Salary Context
This $142K-$178K range is below the median for Data Scientist roles in our dataset (median: $162K across 195 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 11,520 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Timberland, 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 $190,000 based on 1,564 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($160K) sits 16% below the category median. Disclosed range: $142K to $178K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Timberland AI Hiring
Timberland has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Denver, CO, US, Greensboro, NC, US. Compensation range: $130K - $178K.
Location Context
AI roles in Denver pay a median of $200,000 across 143 tracked positions. That's 6% below the national 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.