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About This Role
The Wall Street Journal is seeking a senior data scientist who will articulate, lead and execute on our newsroom’s audience\-data strategy. In this role, you will ideate and guide projects, mentor team members and set data science, analytics, and data engineering best practices. This job involves in\-depth data analysis, data modeling, machine learning and AI experimentation. The results of your work will impact the way we measure, create, produce and distribute our journalism.
You will sit within the Journal’s newsroom data science and insights team and report to the Senior Manager of Newsroom Data. The newsroom data team works across coverage, audience, product and platform strategy. The team focuses on the best way to bring digital innovation to the newsroom and works with our editors as well as product and engineering teams on creating great news experiences that resonate with our audiences, current and future. You will be a close partner to content strategy, audience reach, loyalty and product teams, as well as interface with other data scientists and engineers across the business.
You Will:
- Translate newsroom and business questions into problems to be solved with data and machine learning
- Ingest, standardize, integrate and model audiences using web analytics data
- Develop explanatory and predictive algorithms to optimize content for distribution on and off platform
- Devise and lead data\-driven research and experimentation programs to help newsroom and product teams engage audiences
- Present and share data\-science work with newsroom and business partners
- Lead team discussions on AI, analytics, engineering, visualization and modeling innovations and best practices
- Build frameworks to ensure high quality, trustworthy and insightful data
- Proactively design, roadmap and deliver projects with efficiency and quality assurance processes in mind
You Have:
- An advanced degree in a quantitative field plus 3\+ years experience, or a minimum of 6\+ years of experience executing and leading data\-science projects in areas relevant to the work of the WSJ newsroom data team
- Significant work experience in developing machine\-learning models and deploying them to production environments
- Significant work experience with data orchestration and pipeline tools like dbt or Airflow
- Strong proficiency in Python, git, SQL, relational databases, and experience using Python packages like pandas, scikit\-learn, numpy, tensorflow, etc.
- Expertise in machine\-learning approaches, both supervised and unsupervised, as well as experience applying LLM models to various content types
- Excellent project\-management and organizational skills with a keen attention to detail
- Excellent written and verbal communication, along with strong interpersonal skills
- Ability to think strategically and execute methodically
Standout candidates will also have strengths in one or more areas of the following:
- Experience at a media company
- Knowledge of web tracking platforms like Adobe Analytics, Google Analytics, Parse.ly or Chartbeat
- Experience with data visualization tools like Looker data studio or Tableau
To apply, please submit a resume and a cover letter explaining how your skills, experience and interests align with the expectations of the role by May 8th.
Equal Opportunity Employer
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, national origin, protected veteran status, disability status or any other protected characteristic under applicable law. EEO/Disabled/Vets
Reasonable Accommodation
We are committed to providing reasonable accommodation for qualified individuals with disabilities in our job application and/or interview process. If you need assistance or accommodation in completing your application or participating in an interview due to a disability, email us at talentresourceteam@dowjones.com. Please put "Reasonable Accommodation" in the subject line and provide a brief description of the type of assistance you need. This inbox will not be monitored for application status updates.
Please refer to the privacy notice at the bottom of this page for submitting any data access, deletion, or other data subject rights requests, where permitted under your local laws and regulations.
Business Area: Dow Jones \- News \- WSJ
Job Category: Data Analytics/Warehousing \& Business Intelligence
Union Status:
Union role
Base Pay Range: 115,000 \- 135,000
We’re committed to offering competitive and flexible compensation to attract top talent. This pay range reflects our good faith estimate for the role and may vary based on a candidate’s experience, skills, location, and other relevant factors.
For bonus\-eligible roles, targets are determined based on multiple considerations, including market benchmarks and individual contributions.
For benefits\-eligible roles, we offer a comprehensive and competitive benefits package covering health, retirement, wellbeing, and more, along with optional benefits to meet the diverse needs of our employees.
Since 1882, Dow Jones has been finding new ways to bring information to the world’s top business entities. Beginning as a niche news agency in an obscure Wall Street basement, Dow Jones has grown to be a worldwide news and information powerhouse, with prestigious brands including The Wall Street Journal, Dow Jones Newswires, Factiva, Barron’s, MarketWatch and Financial News.
This longevity and success is due to a relentless pursuit of accuracy, depth and innovation, enhanced by the wisdom of past experience and a solid grasp on the future ahead. More than its individual brands, Dow Jones is a modern gateway to intelligence, with innovative technology, advanced data feeds, integrated solutions, expert research, award\-winning journalism and customizable apps and delivery systems to bring the information that matters most to customers, when and where they need it, every day.
Req ID: 52377
Salary Context
This $115K-$135K range is in the lower quartile for Data Scientist roles in our dataset (median: $166K across 345 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 26,159 AI roles we're tracking, Data Scientist positions make up 2% of the market. At Dow Jones, 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 $204,700 based on 441 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($125K) sits 39% below the category median. Disclosed range: $115K to $135K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Dow Jones AI Hiring
Dow Jones has 1 open AI role right now. They're hiring across Data Scientist. Based in New York, NY, US. Compensation range: $135K - $135K.
Location Context
AI roles in New York pay a median of $200,000 across 1,670 tracked positions. That's 9% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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