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About This Role
Overview:
Cadent ignites seamless connections between brands, publishers \& consumers. Our predictive AI orchestrates outcomes on any platform customers are on, across any media they consume \& at any stage of the journey. To learn more, please visit Cadent.com.
Right now, we are looking for a highly motivated Associate Data Scientist who will be responsible for applying scientific methods to identify business optimization strategies and develop, evaluate and demonstrate prototypes of empirical software including but not limited to machine learning, signal processing and optimization based numerical methods.
Data Scientists collaborate directly with the Business, Product, Data Engineering, and QA team members to productize AI/ML research to drive business growth. This is a critical role that needs knowledge of mathematics and engineering, output from this role will be leveraged by business, engineers and by senior executives to define the future of Cadent.
Responsibilities:
- Design, train and apply statistics, mathematical models, and machine learning techniques to create scalable solutions to business problems
- Work with machine learning engineers and software developers to deploy models and modeling pipelines to be leveraged inside of business software products
- Contribute iterative improvements to predictive models
- Leverage model governance techniques and frameworks to ensure performance and stability of data science products
- Work with product team to align on product roadmap and goals with business vertical KPIs
- Participate in the Agile / scrum process
- Follow the CRISP\-DM process to generate robust documentation associated with iterative work
- Present results and findings to technical audience, product and business stakeholders
- Collaborate effectively with other members of the data science team, engineering research team and broader data services group including but not limited to Machine Learning Engineers, Data Engineers, Analytics Engineers, Software Engineers, Quality Assurance Engineers, and Business Intelligence analysts
- Participate in researching new data, tools, algorithms and tech stack to align with evolving AI \& ML industry
Qualifications:
- M.S. or higher in computer science, mathematics, or related discipline with a focus on machine learning; or the equivalent of 1\-2 years’ experience in a similar role
- Proven background answering open ended research questions using data, tools and technology
- Ability to write clean, expressive code in Python and/or other tools including PySpark, Scala etc.
- Practical experience building, fine tuning and evaluating machine learning models, preferably with the scikit\- learn ecosystem
- Experience with SQL and reading/writing from/to relational databases
- Experience using cloud computing ecosystems (e.g., AWS, GCP) is a plus
- Experience with the practical application of computational statistics
- Fundamental understanding of the mathematical workings of standard feature engineering, dimension reduction, machine learning algorithms and model validation \& measurement
- Demonstrated communication skills including the ability to switch between technical and business contexts
- Familiarity with best practices for software engineering and the use of the scientific Python ecosystem
- Knowledge of GenAI tools and capabilities is a plus
Eligibility:* Must be legally authorized to work in the United States without employer sponsorship now or in the future.
So, if the leading edge of media technology is the place you want to be, please contact us today and let’s start the conversation! Health \& Wellbeing
- Inclusive health, dental, and vision plans built to support diverse lifestyles
- Enhanced support for reproductive health, family planning, and new parents
- Employer contribution to HSA
- Voluntary Benefits: Pet Insurance, LegalEase Legal Assistance, Critical Illness, Hospital Indemnity, and Accident coverages
- Generous and inclusive paid parental leave
- Mental health support and Employee Assistance Program (EAP)
- Free access to the Calm App
- Monthly Wellness reimbursement
- Special discounts through LifeMart and Plum Benefits
- Flexible Time Off (FTO) policy and company breaks
- 11 observed Federal holidays
- Summer Fridays between Memorial Day and Labor Day
Inclusion \& Belonging
- Employee Resource Groups that foster connection and community
- DEI programming and initiatives
- Company\-sponsored events to bring employees together
- Offices across the U.S., including Manhattan, Philadelphia, and San Jose
Financial \& Security
- 401(k) participation with discretionary employer match
- Life, Short\-term, and Long\-term Disability coverage
- Cell phone and WiFi stipend
- Pre\-tax commuter benefits
- Access to certified financial coaches and planning tools
Values* Passionate. We are inspired partners.
- Purposeful. We create with intention.
- Curious. We discover what’s possible.
The salary range for this role takes into account a wide range of factors considered in making compensation decisions including, but not limited to, skill sets, experience and training, licensure and certifications as well as other business and organizational needs. *Cadent is an Equal Opportunity Employer and is committed to supporting all it’s employees when it comes to Inclusion \& Diversity. Cadent’s policy is to provide equal opportunity for applicants \& employees without regard to race, color, religion, creed, gender, gender identity or expression, sexual identity or orientation, age, national origin or ancestry, citizenship, disability or medical condition (including pregnancy, childbirth, or related medical condition), sexual and reproductive health decisions, genetic information, marital status (including domestic partnerships and civil unions), pregnancy, culture ancestry, familial or caregiver status, military status, veteran status, socioeconomic status, unemployment status, status as a victim of domestic violence or any other basis prohibited by law. and will not discriminate against the basis of disability. This commitment is honored when it comes to decisions on hiring, recruiting, training, promotions, compensations, benefits, transfers and terminations.* *Cadent is seeking to actively engage with our employees from a wide variety of cultures and to connect with our clients differently. Our workforce has generational diversity that supports greater innovation when we maximize representation of all diversity. Our active employee resource groups promote engagement across all groups of individuals that are represented within the company and externally.*
Pay Range: USD $90,000\.00 \- USD $110,000\.00 /Yr.
Salary Context
This $90K-$110K range is in the lower quartile for Data Scientist roles in our dataset (median: $157K across 236 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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Cadent, 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 $198,000 based on 808 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $97,880. This role's midpoint ($100K) sits 49% below the category median. Disclosed range: $90K to $110K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Cadent AI Hiring
Cadent has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in New York, NY, US. Compensation range: $110K - $210K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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