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About This Role
Business Title: Director, Data Science
Department: Business Intelligence
Reports to: Vice President, Business Intelligence
FLSA Status: Full\-Time, Exempt
Job Summary
The Sacramento Kings are committed to using data and technology to better understand our fans, strengthen business performance, and improve decision\-making across the organization. As part of the Business Intelligence team, the Director of Data Science will lead advanced analytics initiatives that translate complex data into actionable recommendations across ticketing, retail, marketing, and other areas of the business.
This role will serve as a key analytical partner to leaders across the organization, with responsibility for ticket pricing, inventory management, revenue forecasting, and customer analytics. The Director will develop and maintain predictive and analytical solutions, build automated tools and data pipelines, and identify new opportunities to use data science and emerging technologies to improve business outcomes.
The ideal candidate combines strong technical expertise with commercial judgment and the ability to communicate complex analytical concepts clearly to both technical and non\-technical audiences. This position will also lead and develop members of the Business Intelligence team while remaining hands\-on in the analysis, development, and implementation of solutions.
Key Responsibility Areas
- Lead the development and execution of analytical strategies supporting ticketing revenue, including single\-game dynamic pricing, season and plan pricing, inventory management, sell\-through analysis, and revenue forecasting.
- Develop analytical models and decision\-support tools that identify pricing and inventory opportunities and provide actionable recommendations to Ticket Sales, Ticket Operations, Marketing, and organizational leadership.
- Build, maintain, and enhance predictive and statistical models related to customer behavior, including areas such as retention, purchase propensity, demand forecasting, customer segmentation, and revenue optimization.
- Conduct scenario analysis and ad\-hoc analytical work to support time\-sensitive business decisions and identify emerging revenue opportunities.
- Develop automated solutions that improve the speed, accuracy, and scalability of recurring business processes, including ticket pricing, price code creation, inventory analysis, reporting, and other analytical workflows.
- Design and maintain data pipelines that integrate business data from ticketing, retail, food \& beverage, digital, and other third\-party platforms into the organization's data environment.
- Partner with internal technology resources and external data partners to ensure data used for analytics is accurate, timely, accessible, and appropriately structured.
- Develop dashboards, reporting tools, and analytical frameworks that enable stakeholders to monitor performance, understand key business drivers, and make informed decisions.
- Evaluate opportunities to apply machine learning, AI, optimization, and other emerging analytical techniques to business challenges where they can create measurable value or improve operational efficiency.
- Lead, mentor, and develop direct reports within the Business Intelligence team, establishing clear priorities while supporting professional growth and technical development.
- Foster collaboration and knowledge sharing across the Business Intelligence team, helping strengthen analytical capabilities and best practices throughout the department.
Qualifications
- Bachelor's degree in Data Science, Statistics, Mathematics, Economics, Computer Science, Operations Research, Engineering, or another quantitative field; advanced degree preferred.
- 8\+ years of professional experience in data science, advanced analytics, business analytics, or a related quantitative field, with demonstrated experience using analytics to influence business decisions.
- 3\+ years of experience leading, managing, or mentoring analysts or data science professionals preferred.
- Proficiency in SQL as well as Python and/or R, with demonstrated experience developing production\-quality analytical models, automated workflows, and data solutions.
- Strong understanding of statistical modeling, forecasting, optimization, experimentation, and machine learning techniques, with the ability to select methodologies appropriate to the business problem.
- Experience developing analytical solutions that have influenced pricing, revenue management, inventory optimization, customer behavior, marketing, or other commercial decisions.
- Experience working with relational databases, cloud data platforms, data warehouses, and data integration processes.
- Experience with Tableau or similar business intelligence and data visualization platforms.
- Strong understanding of analytical development practices, including model validation, documentation, reproducibility, version control, and code quality.
- Strong business acumen and curiosity, with the ability to understand business problems before determining the appropriate analytical solution.
- Ability to manage multiple priorities and balance longer\-term analytical development with time\-sensitive business needs.
- Experience in sports, entertainment, ticketing, retail, hospitality, or another dynamic consumer business preferred.
- Familiarity with Ticketmaster Archtics/Host or other ticketing platforms preferred.
Compensation:
Base Salary Range: $110,000–$140,000, plus eligibility for an annual discretionary bonus.
Total Rewards Package:
In addition to a competitive compensation package, we offer a comprehensive suite of benefits designed to support your health, financial well\-being, and work\-life balance, including:
- Medical, dental, and vision coverage for employees and eligible dependents
- 401(k) with company matching contributions
- Self\-Directed Time Off, 11 paid holidays, and Summer Fridays
- Paid parental leave
- Company\-paid life insurance and long\-term disability coverage
- Employee Assistance Program and wellness resources
- Cell phone stipend
- Sacramento Kings season tickets, concert access, team store discounts, parking, and other exclusive team member perks
Salary Context
This $110K-$140K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Sacramento Kings, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($125K) sits 42% below the category median. Disclosed range: $110K to $140K.
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.
Sacramento Kings AI Hiring
Sacramento Kings has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Sacramento, CA, US. Compensation range: $140K - $140K.
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 AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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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