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About This Role
Job Description - Jr. Data Scientist
CATCH Intelligence is a Business Intelligence, Reporting, Analytics, and Data Warehouse consulting firm based in the Midwest. We help business transform raw data into information, information into knowledge, and knowledge into plans that drive profitable action!
We are seeking a highly motivated and analytical Data Scientist to join our team!
In this role, you will partner with clients across public and private sectors to solve complex business challenges using data, advanced analytics, machine learning, artificial intelligence, and custom technology solutions.
The ideal candidate has experience applying a variety of data mining and analytical methods, leveraging modern data tools, building and implementing statistical and machine learning models, developing algorithms, and conducting simulations to support decision-making. They possess a proven ability to translate data into actionable insights that drive measurable business outcomes. The successful candidate is comfortable working with stakeholders at all levels, collaborating across functional teams, and communicating complex technical concepts to both technical and non-technical audiences. Most importantly, they are passionate about uncovering meaningful insights from large and complex datasets and helping organizations improve performance through data-driven solutions.
Key Responsibilities
- Partner with clients to understand business objectives, operational challenges, and strategic priorities.
- Translate business problems into analytical frameworks and data science solutions.
- Collect, cleanse, transform, and analyze structured and unstructured data from multiple sources.
- Develop predictive, prescriptive, and descriptive analytics solutions.
- Design, train, evaluate, and deploy machine learning models.
- Build and validate statistical models to support forecasting, optimization, classification, clustering, and anomaly detection.
- Create dashboards, reports, and visualizations that communicate actionable insights to technical and non-technical audiences.
- Collaborate with software developers, data engineers, business analysts, and client stakeholders throughout project delivery.
- Present findings, recommendations, and technical concepts to executives and project teams.
- Support proposal development, client presentations, and business development activities.
- Maintain awareness of emerging technologies, industry trends, and best practices in data science and artificial intelligence.
- Ensure solutions adhere to data governance, privacy, security, and ethical AI principles.
- Develop AI and Generative AI solutions using modern cloud-based technologies.
Qualifications for Data Scientist
- 0-3 years of experience applying data science, machine learning, or advanced analytics in a professional environment.
- Bachelors or Masters in Statistics, Mathematics, Computer Science or another quantitative field
- Proficiency in Python, R, SQL, or similar analytical programming languages.
- Strong understanding of statistical analysis, predictive modeling, and experimental design.
- Strong problem-solving, critical thinking, and communication skills.
- Ability to explain technical findings to non-technical audiences.
- Ability to manage multiple projects and priorities in a consulting environment.
- Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
- Excellent written and verbal communication skills for coordinating across teams.
- A drive to learn and master new technologies and techniques.
Preferred Qualifications for Data Scientist
- Experience with cloud platforms such as Microsoft Azure, AWS, or Google Cloud.
- Experience with Azure AI Services, Azure OpenAI, Microsoft Fabric, Azure Machine Learning, or similar AI platforms.
- Knowledge of Generative AI, LLMs, and AI application development.
- Experience developing custom applications that integrate AI and analytics capabilities.
- Familiarity with MLOps, DevOps, and model deployment methodologies.
- Consulting experience and client-facing project delivery experience.
- Experience working with government agencies, regulated industries, or enterprise organizations.
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 11,520 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Catch Intelligence, 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 1,564 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $112,320.
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.
Catch Intelligence AI Hiring
Catch Intelligence has 1 open AI role right now. They're hiring across Data Scientist. Based in Omaha, NE, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 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).
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 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.
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