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About This Role
Description:
Senior Data Scientists at Brunner lead advanced analytics projects across multiple Brunner clients. They are also instrumental in contributing to the agency’s R\&D initiatives, where we apply cutting\-edge approaches to maintain our best\-in\-class marketing services.
As a Senior Data Scientist, you work directly with internal teams and external clients to help those clients grow their businesses through optimized marketing. You accomplish this with your expertise in applied statistical techniques, high\-quality production deployment, and effective communication. Your projects address a variety of marketing problems such as identifying best markets or customers, estimating, optimizing, and forecasting marketing campaign performance, processing unstructured data, and predicting behavior. Data management tasks include data acquisition from a variety of sources, ingestion, transformation, loading, and quality control.
You are adept across the full analytics project lifecycle, from requirements and scoping, through data acquisition and management, EDA, model fitting and selection, insight generation, communication to stakeholders, and deployment. You support the improvement of that project lifecycle by helping to create or develop proprietary software tools. Senior Data Scientists are trusted confidants of clients, internal leaders, and are responsible for project success.
Responsibilities
- Selecting features, building, and optimizing models using machine learning and AI
- Data mining
- Expanding and improving our data resources
- Processing, cleansing, and verifying the integrity of data used for analysis
- Developing clear and meaningful presentations with effective, audience\-relevant visualizations
- Creating automated systems and tracking their performance
- Making critical contributions to the development of new, innovative tools and services
- Building client rapport and identifying new opportunities for engagements
Requirements:
Skills and Qualifications
- In\-depth, hands\-on experience with commonly used modeling procedures including regression, classification, forecasting, and optimization
- Understanding of how to apply supervised and unsupervised machine learning algorithms, and regular self\-improvement to update your knowledge
- Proficiency with Python, SQL, relational databases, and an IDE such as VS Code or PyCharm
- Proficiency in the application of AI tools (LLM prompts, agents, and coding extensions) to improve the efficiency and quality of your workflow
- Experience with data visualization tools such as Tableau, PowerBI, or related Python packages
- Ability to integrate code with REST APIs and external data sources; comfortable working with API documentation
- Preferred candidates will also have experience with:
+ Building and deploying data pipelines and ETL workflows
+ Processing unstructured data such as text or images
+ Cloud environments such as AWS, Azure, or Google Cloud
+ Version control systems such as GitHub
Education and Training
- 3 – 5 years of experience in a data science role
- Bachelor’s or Master’s degree in program with heavy emphasis on quantitative analysis or programming, such as analytics or data science, economics, physics, mathematics or statistics, computer science, engineering, or quant emphasis in social science (psychology, sociology, policy, etc.)
Who We Are
Brunner is an integrated marketing communications agency with a fast\-paced, creative environment. We value Perseverance, Integrity, Tenacity, Curiosity, and Heart in every aspect of our business, especially our team members. We are good people creating good work. Brunner’s commitment is to its people \- fostering growth within (robust training, low attrition, and commitment to diversity), becoming an extension of their client’s teams and maintaining long\-term relationships, and creating great marketing programs that achieve business impact for their clients.
Perks
We offer your full traditional benefits including health, dental, vision, a 401k plan, and life insurance. We also offer commuter and transit benefits, as well as an employee wellness benefit for you to enjoy your life outside of work. Add to that unlimited PTO, half day summer Fridays and all the holiday time off you would expect.
Flexible Work Policy
It's simple \- work where it's most convenient for you! We recognize that we're a mixed group of people with different priorities in our lives, so we empower our employees to create their ideal working scenario...in\-office, at home, or a hybrid of both. Keep in mind you might need to pop into the office for a client meeting here and there, or even an agency\-wide meeting.
Environmental Demands
Normal office environment. Ability to sit for extended periods of time including extensive use of PC equipment.
We like everyone, so we are an equal opportunity employer. We do not consider your race, religion, ethnicity, national origin, age, sex, disability, marital status, sexual orientation, or veteran status when deciding to hire you. We just want you to be you.
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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At M J Brunner Inc, 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 $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
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.
M J Brunner Inc AI Hiring
M J Brunner Inc has 1 open AI role right now. They're hiring across Data Scientist. Based in Pittsburgh, PA, US.
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 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 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).
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 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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