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About This Role
### Redefine the future of customer experiences. One conversation at a time.
At Nextiva, we're reimagining how businesses connect, bringing together customer experience and team collaboration on a single, conversation centric platform. Powered by AI, driven by human innovation.
Our culture is forward thinking, customer obsessed and built on the belief that meaningful connections drive better business outcomes. Whether it's through our signature Amazing Service®, the technology we create, or the experiences we cultivate, connection is at the core of who we are.
If you're ready to collaborate with incredible people, make an impact, and help businesses everywhere deliver truly amazing experiences, this is where you belong.
Location: This is an onsite role based at Nextiva's Scottsdale headquarters (9451 E. Via de Ventura, Scottsdale, AZ 85256\). Working together onsite strengthens how we operate, enabling faster decisions, clearer communication, and stronger execution, so you can make a greater impact and move work forward with speed and clarity.
In\-Office Expectation: This role is expected to work onsite four days per week. Specific scheduling and flexibility will be guided by your leader to support both team collaboration and individual productivity.
The Role
Nextiva spends tens of millions of dollars on marketing every year. We generate billions of customer conversations. We have one of the richest GTM datasets in B2B SaaS. Most of that data still sits in dashboards no one reads, attribution models no one trusts, and weekly reports nobody acts on. We're going to change that.
This is not a reporting role. This is not a dashboard management role. This is the role that turns marketing data into decisions. You'll partner with Marketing, RevOps, BI, and Finance to rebuild how the company sees marketing performance. You'll be the analyst whose work the CMO and CFO cite in the boardroom, and the one Marketing leadership calls when the question is hard and the stakes are real.
This is an individual contributor role with high visibility and influence across the organization. You'll have the reach of a director and the autonomy of an analyst.
What You'll Own
Marketing Intelligence and Strategic Analytics:
- Analyze marketing, pipeline, and revenue performance to surface trends, risks, opportunities, and the drivers behind them
- Conduct deep\-dive analyses to answer the ambiguous business questions that don't fit in a dashboard
- Translate data into clear, actionable recommendations for marketing leadership
- Tell the org not just what happened, but why it happened and what to do next
- Build scalable frameworks for measuring marketing effectiveness across channels, campaigns, and funnel stages
Marketing Data Strategy and Architecture:
- Partner with Marketing Ops, RevOps, and BI to evolve our marketing data ecosystem into something scalable, trustworthy, and analysis\-ready
- Improve the quality, accessibility, and consistency of marketing performance data
- Identify gaps, inefficiencies, and structural problems in how we measure, and lead the work to fix them
Forecasting, Modeling, and Performance Insights:
- Support forecasting, pacing, and pipeline analysis across the marketing org
- Analyze funnel conversion trends, investment efficiency, and performance drivers
- Build models that improve visibility into marketing contribution and business impact
- Help leadership evaluate scenarios, tradeoffs, and investment decisions with confidence
AI\-enabled Analytics and Operational Intelligence:
- Leverage modern AI tools and workflows to accelerate insight generation and decision velocity
- Use automation and AI\-assisted analysis to uncover trends, anomalies, and optimization opportunities at scale
- Apply strategic judgment and analytical rigor to validate findings before they become recommendations
- Help evolve marketing toward a proactive, intelligent analytics function rather than a reactive reporting one
What Success Looks Like
- Marketing leadership trusts the data and acts on it
- Hard questions get answered faster, with sharper clarity
- Marketing data becomes accessible, consistent, and useful across the org
- Reporting evolves from dashboards\-after\-the\-fact to intelligence\-before\-the\-decision
- Forecasting and performance visibility improve quarter over quarter
- Data\-driven decision\-making becomes the default, not the exception
What We're Looking For
- 3\+ years in marketing analytics, marketing intelligence, data science, or related analytical roles
- Strong SQL and hands\-on data analysis. You don't need someone to pull the number for you
- Experience with BI and visualization platforms: Tableau, Looker, Power BI, or equivalents
- Familiarity with CRM, marketing automation, and modern data warehouse environments
- AI\-native. You use Claude, ChatGPT, and the rest of the stack to do more, faster, every day
- Excellent business judgment. You know when to push a model further and when to ship the answer
- Strong communication. You can write a finding the way a journalist writes a headline: clear, sharp, undeniable
- Comfortable operating in ambiguous, fast\-moving environments
Preferred
- B2B SaaS or GTM analytics experience
- Cross\-functional work with RevOps, Finance, and Marketing leadership
- Attribution modeling, funnel analysis, forecasting, or investment modeling experience
- Exposure to modern data stack environments and analytics engineering concepts
- Predictive analytics or statistical modeling background
The Bar
You think most marketing analytics work is bad. Noisy, late, ignored. You want to build the gold standard. You want a CMO and CFO who reach for your dashboards before they reach for their gut. You want to be the analyst other analysts ask for advice. You're not waiting for permission to make marketing smarter. That is the job.
Nextiva DNA (Core Competencies)
Nextiva's most successful team members share common traits and behaviors:
- Drives Results: Action\-oriented problem solvers who quickly bring clarity and simplicity to ambiguity, challenge the status quo, and lead meaningful change; celebrating wins to fuel momentum. They act swiftly and pragmatically, learning and improving as they go.
- Critical Thinker: Data\-driven, forward\-thinking individuals who identify key drivers, anticipate risks, and deliver clear recommendations. They confidently leverage AI and automation to reduce friction, improve decision\-making, and focus on higher\-value work.
- Right Attitude: Collaborative, competitive, and resilient team players who jump in to solve tough problems, learn from setbacks, and foster a culture of service, respect, and care for customers and teammates.
#### Total Rewards
Our Total Rewards offerings are designed to allow Nexties to take care of themselves and their families so they can be their best, in and out of the office.
Our compensation packages are tailored to each role and candidate's qualifications. We consider a wide range of factors, including skills, experience, training, and certifications, when determining compensation. We aim to offer competitive salaries or wages that reflect the value you bring to our team. Depending on the position, compensation may include base salary and/or hourly wages, incentives, or bonuses.
- Health: Multiple health plan options to suit your needs, including medical, dental, vision, and telemedicine coverage
- Insurance: Life, disability, and supplemental indemnity plans
- + ️ Work\-Life Balance: Flexible Time Off for salaried employees, PTO for hourly employees, Paid Sick Time, Paid Parental Bonding Leave, and holiday pay
- Financial Security: 401(k) with company match, Health Savings Accounts with company contributions, Dependent Care FSA
- Wellness: Employee Assistance Program (EAP) and comprehensive wellness initiatives
- Growth: Access to ongoing learning and development opportunities and career advancement
At Nextiva, we're committed to supporting our employees' health, well\-being, and professional growth. Join us and build a rewarding career!
Nextiva is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We prohibit discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Nextiva participates in the E\-Verify Program where and as required by law. For additional information about E\-Verify visit USCIS.
\#LI\-MS1 \#LI\-Onsite
Founded in 2008, Nextiva has grown into a global leader trusted by over 100,000 businesses and 1M\+ users worldwide. Headquartered in Scottsdale, Arizona, and with teams across the globe, we're the future of customer experience and team collaboration through our AI\-powered, conversation\-centric platform.
Want to see what life at Nextiva is all about? Connect with us on Instagram, Instagram MX, YouTube, LinkedIn, and the Nextiva Blog.
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 Nextiva, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
Nextiva AI Hiring
Nextiva has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Scottsdale, AZ, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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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