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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.
Spectrum Reach® provides custom advertising solutions for local, regional and national clients. Spectrum Reach creates scalable advertising and marketing services driven by aggregated and de-identified data insights and award-winning creative services. Spectrum Reach helps businesses of all sizes reach anyone, anywhere, on any screen. Additional information about Spectrum Reach can be found at https://www.spectrumreach.com
As the Lead Data Scientist, you are responsible for leading the development of data-driven solutions for Spectrum Reach’s advertising sales business. Utilizes analytical, statistical, and programming skills to clean, aggregate, and analyze large data sets and interpret results. This position requires a strong command of statistical techniques machine learning algorithms, and big data technologies. The ideal candidate will possess a demonstrated practical ability to determine where to invest time, and depending on the assignment, to build sustainable predictive models and services or to synthesize actionable findings and present findings to audiences with diverse agendas and varying levels of technical expertise. How You’ll Make an Impact
- Actively and consistently support all efforts to simplify and enhance the customer experience.
- Plan and lead the complete analytics life-cycle for problem solving, including: requirements gathering, problem formulation, data grooming, data exploration, model prototyping, model validation, and algorithm productionalization.
- Leverage knowledge in analytical and statistical algorithms to help stakeholders explore methods to improve their business. Utilize experience and feedback to recommend options during the development/research/analysis phase.
- Lead large-scale exploratory data analyses for new data sources or new uses for existing data sources. Establish links across existing data sources and find new, interesting data correlations.
- Plan effectively to ensure analytics products are flexible, modular, and both leverage and contribute to the team’s existing reusable code base.
- Exercise thought leadership and discretion in tailoring the tools, approaches, and data used to meet the needs of the particular problem.
- Responsible for interpretation of results – for both causal inferences and predictive effectiveness.
- Synthesize appropriate recommendations for action and changes.
- Present findings, suggested actions and changes to a broad audience, and manage follow-ups and execution.
- Help teach and explain techniques and tools used to a broad set of business-intelligence, data, and analytics professionals with varied backgrounds.
- Create project plans to help team achieve defined project goals within deadlines; proactively communicate status and escalate issues as needed.
- Mentor and train junior team members as well as assist leadership in setting strategic vision.
- Perform other duties as assigned.
WHAT YOU’LL BRING TO SPECTRUM REACH* Demonstrated mastery and experience with languages such as R and/or Python (including relevant packages) in support of advanced analytics and AI/ML
- Demonstrated experience with SQL, large data sets and the tools to efficiently obtain, transform, and store large volumes at scale. Firm understanding of data architecture and data warehousing techniques
- Experience and proficiency with commercial cloud platforms such as AWS, Azure, GCP
- Demonstrated experience and solid theoretical foundation of the properties of the major families of machine learning models, including regression, classification, clustering, neural networks, and large language models
- Experience with large scale multi-series time series forecasting
- Command of advanced mathematical concepts including calculus, PDEs, probability, and statistics, and the ability to independently learn any necessary additional concepts
- Effective synthesis and presentation skills
- Ability to communicate results and recommendations to a wide variety of audiences
- Demonstrated ability and desire to continually expand skill set, and learn from and teach others Required Education
Bachelor's degree in computer science, statistics, operations research and/or equivalent combination of education and experience Required Related Work Experience
7+ years data manipulation and statistical modeling as a Scientist, Consultant, Architect, DBA, or Engineer
7+ years SQL/R/Python Programming PREFERRED QUALIFICATIONS
- Experience in the advertising sales, cable and/or telecommunications industry
- Advanced Python knowledge and deep experience in the AI/ML ecosystem surrounding Python
- Experience in Operations-research/optimization, in particular mixed-integer programming, multi-objective optimization, CPLEX and Gurobi solver configuration and performance tuning
- Experience in AWS ecosystem (including EMR, Glue, Athena, SageMaker, Bedrock)
- Experience in Snowflake architecture, resource optimization, and performance tuning
- Experience in distributed computing paradigms and parallel processing, including Spark optimization techniques and performance tuning
- Experience in MLOps, including end-to-end ML pipelines with automated training, validation, and deployment, model registry systems for versioning and governance
- Experience with development of systems using generative AI, reasoning models and agentic AI methods. Preferred Education
Master’s Degree in related field
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BDA309 2026-82130 BDA309 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 $89,800.00 and $229,300.00. 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 $89K-$229K range is below the median for Data Scientist roles in our dataset (median: $167K across 172 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 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At Spectrum, 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 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($159K) sits 16% below the category median. Disclosed range: $89K to $229K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Spectrum AI Hiring
Spectrum has 1 open AI role right now. They're hiring across Data Scientist. Based in New York, NY, US. Compensation range: $229K - $229K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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