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About This Role
Connexus Credit Union - Who We Are:
Serving members across all 50 states, Connexus Credit Union is a member-focused cooperative that is proud to return profits to member-owners through high yields for checking accounts and deposit products, as well as competitive rates on our loans. We are a remote first employer with the majority of our employees residing in the upper Midwest.
As an employer we foster collaboration and high performance to achieve excellence. We holistically care for and develop our employees to thrive personally and professionally. We are proud to share our success with our employees and those we serve.
Connexus offers an Amazing Benefits package:
- 25 days of paid time off and 10 paid holidays
- 16 hours of paid Volunteer Time Off
- 401K Retirement with up to 6% employer match
- Excellent Health, Dental, Vision insurance, including multiple plan options
- Health Savings Account with generous employer contributions
- Employer paid Life insurance, Short-Term and Long-Term Disability
- Tuition Reimbursement from $4,000 - $7,000 per calendar year
- Robust Learning and Development program that includes an annual professional development stipend
About the Role:
We are looking for a Data Scientist who enjoys solving business problems and using data to help people make better decisions. You'll partner with teams across the organization to identify opportunities, build models, and turn data from Snowflake and other sources into recommendations that drive business outcomes. You will also play a key role in building our conversational analytics capabilities and expanding how we use AI to better serve our members and employees. We're looking for someone who is curious, detail-oriented, and motivated by making a difference.
Responsibilities:
- Develops data science solutions that utilize machine learning, artificial intelligence, statistical and quantitative modeling to develop predictive and prescriptive analytics.
- Applies and integrates statistical, mathematical, predictive modeling and business analysis skills to manage and manipulate complex data from a variety of sources.
- Creates visualizations, including dashboards, to display data-backed insights and predictive results.
- Works across a wide range of related areas including data extraction and cleansing, feature engineering, machine learning, exploratory analysis, data quality analysis, experimental analysis.
- Partners with other teams to create and deploy Data Products that create member and/or business value.
- Executes data science and analytics projects, including, but not limited to enterprise projects, run-the-business, and other special projects.
- Participates in and communicates the status of projects with respect to milestones and schedule commitments.
- Leads data discovery sessions and documents needs in a way that can translate into work requirements for other team members.
- Elicits requirements using interviews, document analysis, requirements sessions, data descriptions and definition, use cases, scenarios, and data analysis.
- Validate hypotheses, identify growth opportunities, and influence direction for process improvement with analyses, experimentation or other scientific methods.
- Develops quality assurance test plans and direct quality assurance testing.
- Adheres to data science best practices, documentation requirements, and BT standards and guidelines.
- Prepares and delivers reports, recommendations, or alternatives that address existing and potential trouble areas across the organization.
- Adheres to data science policies and procedures.
- Works closely with data engineers, analysts, and team members to support data-driven projects.
- Mentors data analysts.
- Fosters business and IT relationships to understand key needs and form collaborative strategic partnerships.
- Seeks opportunities to improve department efficiency and reduce expenses by streamlining operations.
- Comply with all Federal Regulations as they pertain to your job duties including Bank Secrecy Act.
Position Requirements:
- This position is Remote.
- Bachelor’s degree in data science, applied mathematics, statistics, or machine learning is Required.
- 5+ years of prior experience in data scientist role is Required.
- 1 + year of data science roles or equivalent experience including working with stakeholders is Required
- Experience applying statistical modeling, machine learning (ML/AI) algorithms for supervised & unsupervised learning, and natural-language processing (NLP) to business problems and goals is Required.
- Proficient in SnowFlake, Python, R, SQL, and basic machine learning is Required.
- Proficient in using data visualization tools. Excellent communications skills, including the ability to simplify recommended action plans from complex data science project is Required.
- Must be able to understand and analyze data and rapidly generate actionable results.
Ability to influence and communicate complex analysis & insights to a non-technical audience. *
Connexus Credit Union's Employer Recognitions:
- 2026 Best Place to Work in IT, Computer World
Equal Opportunity Employer/Disabled/Veterans/41 CFR 60–1.4, 41 CFR 60-1.35
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 CONNEXUS CREDIT UNION, 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. Mid-level AI roles across all categories have a median of $193,200.
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.
CONNEXUS CREDIT UNION AI Hiring
CONNEXUS CREDIT UNION has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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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