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About This Role
This role requires the ability to work lawfully in the U.S. without employment-based immigration sponsorship, now or in the future.
The Head of Data Science builds the models that turn a new AI-native behavioral intelligence platform into products customers buy, and proves to those customers that the products work. At the core of the platform, part of Spectrum Intelligence Ventures, is a proprietary transformer-based model that learns how households behave from network-scale signal and encodes that behavior as embeddings. Those embeddings are the foundation for audiences, insights, measurement, and data licenses sold to brands, agencies, and ad tech, media, and AI platforms. Over the next 18 to 24 months, this leader’s job is to get that portfolio to market and make it stand up to buyer scrutiny: build the predictive models and products on top of the embedding layer, set the evidence standard buyers will test them against, and work alongside product and sales to scope pilots, answer technical buyers, and convert evaluations into contracts. In parallel, this leader owns the direction of the embedding model itself, working with the Head of Technology and external development partners, and is accountable for showing that it delivers performance conventional approaches cannot. The Head of Data Science builds and leads the data science team, sits on the platform leadership team, and reports directly to the Head of Intelligence Ventures. HOW THE HEAD OF DATA SCIENCE MAKES AN IMPACT* Build and run the predictive models behind the platform’s audience and data products, including in-market, life-stage, purchase propensity, category interest, lifestyle affinity, and behavioral velocity models scored daily at the household level across more than two dozen verticals. Own segment definitions, coverage, refresh cadence, and measured accuracy for every audience the platform sells, whether through Spectrum Reach, DSP data marketplaces, or partner platforms.
- Own the platform’s Feature Store as a commercial product: named, typed, versioned, and documented features that serve audiences, insights, measurement, and partner models. Lead the outcome anchoring program that trains and calibrates models against licensed third-party outcome data (vehicle registrations, mortgage originations, card and SKU-level retail spend, travel bookings, and similar sources) so that propensity scores mean what they say, and automate feature creation as new signal sources come online.
- Lead the modeling behind custom and partner-specific audiences, including seed-based lookalike expansion, profile-based audience construction, and site- or CRM-seeded models that identify households entering a brand’s purchase path before they reach the brand. Establish repeatable, clean-room-based workflows with transparent match rates, expansion multiples, and performance against the buyer’s own baseline.
- Design and defend the platform’s measurement methodology, including exposed-versus-control campaign lift using matched behavioral controls, test-and-holdout incrementality, and closed-loop validation against real-world outcomes. Define the journey, demand, and category metrics that power insights subscriptions and aggregate demand indices, built to withstand scrutiny from brand marketing science teams, agency analytics groups, and measurement partners.
- Lead the data science behind the platform’s identity and viewing products: the adjudication and confidence calibration methods that verify partner household graphs against network ground truth, and the household viewing-state, trajectory, and reachability models that underpin viewing intelligence for programmers, agencies, and measurement companies.
- Direct the roadmap for the platform’s proprietary transformer-based behavioral embedding model, a multi-entity architecture that encodes both a stable household profile and short-term intent, working with the Head of Technology and external development partners on architecture, training, and refresh. Hold the embedding layer to a commercial standard: measured lift over attribute-only and engineered-feature baselines in the audiences, lookalikes, measurement, and partner models it feeds.
- Establish the evaluation, MLOps, and responsible AI standards for every model the platform ships, including evaluation cards that state accuracy, calibration, and coverage in a form buyers can cite; automated release gates; monitoring and drift detection; bias and fairness testing; and privacy-preserving techniques such as audience floors, differential privacy, and adversarial testing, in partnership with Legal and Privacy.
- Serve as the senior data science voice with customers and partners, including brand analytics and insights leaders, agency data science teams, and DSPs, identity providers, measurement companies, and AI platforms. Scope pilots and evaluations with product, sales, and solutions teams, and translate methodology into clear commercial proof points.
- Build and lead a team of data scientists, ML engineers, and measurement scientists matched to the work ahead: strong in applied predictive modeling and measurement, with the deep learning depth to take greater ownership of the embedding layer over time. Set hiring priorities, operating cadence, and a culture of rigorous experimentation with a clear bias toward production deployment and revenue impact.
REQUIRED QUALIFICATIONS* Proven record leading data science for data, audience, identity, targeting, or measurement products in the advertising, media, or marketing ecosystem, where models are packaged, priced, sold, and renewed by external buyers
- Deep, hands-on expertise in predictive modeling on consumer-level data at scale, including propensity and response modeling, classification, gradient boosting, regression, calibration, segmentation and clustering, and lookalike modeling, with sound judgment about which method fits the commercial problem
- Strong grounding in measurement and causal inference, including incrementality testing, matched and synthetic controls, test-and-holdout design, and the practical limits of attribution and media mix approaches
- Working knowledge of identity and data collaboration: household and device graphs, onboarding and match rates, clean rooms (Snowflake, LiveRamp, AWS, or equivalent), identity namespaces such as RampID and UID2, and the use of licensed third-party data in modeling
- Command of the full data science lifecycle in production, from feature engineering through training, validation, deployment, monitoring, and iteration, at large dataset scale (billions, even trillions of records); experience designing feature stores and scoring pipelines that serve audiences and partner deliveries
- Hands-on proficiency with Python, SQL, and distributed data processing (Spark or equivalent); experience running ML workloads on Snowflake, Databricks, AWS, or equivalent cloud platforms
- Solid working knowledge of deep learning and representation learning, including embeddings, sequence models, and transformers, sufficient to direct an embedding roadmap with external partners, set its evaluation standards, and put embeddings to work as features in downstream models
- Familiarity with privacy-preserving analytics and consumer data regulation, including aggregation thresholds, differential privacy, and consent management; comfortable working alongside Legal and Privacy on product design
- Ability to explain methodology clearly to executives, sellers, and buyers, and to defend it under scrutiny from sophisticated technical counterparts in external partner and customer conversations
- Track record of building and growing high-performing data science teams, ideally from an early stage, and recruiting senior talent in competitive markets
- Curiosity about the application of AI to consumer intelligence, including how foundation models and AI agents can draw on behavioral context to make better decisions
REQUIRED EDUCATION* Bachelor’s Degree in Computer Science, Statistics, Mathematics, Economics, or a related quantitative field
PREFERRED EDUCATION* Master’s degree or PhD in Statistics, Machine Learning, Econometrics, Operations Research, or a related quantitative discipline; an advanced degree is valued but not required, and a record of applied commercial impact is weighted equally
REQUIRED RELATED WORK EXPERIENCE AND NUMBER OF YEARS* Experience leading applied data science or analytics teams that build data, audience, identity, or measurement products: 7 years
- Hands-on predictive modeling, machine learning, or measurement science on consumer-level data at scale: 10 years
PREFERRED RELATED WORK EXPERIENCE AND NUMBER OF YEARS* Senior data science leadership at a data, identity, audience, or measurement company in the advertising and media ecosystem (e.g., LiveRamp, Merkle, Epsilon, Acxiom, Experian, TransUnion, Nielsen, Circana, Comscore, The Trade Desk, or specialty analytics and measurement firms): 10 years
- Applied deep learning or representation learning (embeddings, sequence models) deployed in production on consumer behavioral data: 3 years
- Work with TV and video viewership data, cross-screen measurement, or identity graph quality evaluation: 3 years
WORKING CONDITIONS* In-office position preferably based in New York City
- Travel as required for partner engagements, executive meetings, and industry events
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XEN421 2026-85457 XEN421 2026
Here, our employees don’t just have jobs, they're building careers. That’s why we offer a comprehensive pay and benefits package that rewards employees for their contributions to our success, supporting all aspects of their well-being at every stage of life.
A qualified applicant’s criminal history, if any, will be considered in a manner consistent with applicable laws, including local ordinances.
The base pay for this position generally is between $263,200.00 and $393,800.00. The actual compensation offered will carefully consider a wide range of factors, including your skills, qualifications, experience, and location. We comply with local wage minimums and also, certain positions are eligible for additional forms of other incentive-based compensation such as bonuses.
Get to Know Us Charter Communications provides superior communication and entertainment products for residential and business customers through the Spectrum brand. Our offerings include Spectrum Internet®, TV, Mobile and Voice. Beyond our connectivity solutions, we also provide local news, programming and regional sports via Spectrum Networks and multiscreen advertising solutions via Spectrum Reach. When you join our team, you’ll be keeping our customers connected to what matters most in 41 states across the U.S. Watch this video to learn more.
Grow Your Career Here We’re committed to growing a workforce that reflects the customers and communities we serve – providing opportunities for employment and advancement to all team members. Spectrum is an Equal Opportunity Employer, including job seekers with disabilities and veterans. Learn about Life at Spectrum.
Salary Context
This $263K-$393K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Spectrum, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($328K) sits 55% above the category median. Disclosed range: $263K to $393K.
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.
Spectrum AI Hiring
Spectrum has 4 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Stamford, CT, US, New York, NY, US, Stamford, CT. Compensation range: $245K - $393K.
Location Context
AI roles in New York pay a median of $221,000 across 3,257 tracked positions. That's 4% above the national 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 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).
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 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
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