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About This Role
Meet the Moment with Alteryx
We're living through a once\-in\-a\-generation shift in how work gets done. Data, automation, and AI are quickly becoming the center of every business decision \- and Alteryx is leading the transformation.
You'll be working on the challenges that sit at the heart of modern business. No matter your role, the work you do will help organizations move faster, see more clearly, and tackle questions that used to feel impossible.
If you're ready to meet the moment with innovation, curiosity, and excellence, there's a place for you here.
Alteryx is searching for a Sr. Data Scientist . This position is remote\-friendly.
Primary Responsibilities:
- Responsible for designing, developing, and deploying intelligent agents using LangChain and LangGraph frameworks to optimize conversational AI workflows.
- Build, deploy, and optimize data models focused on generative AI and large language models.
- Create and refine methods to measure AI model performance and impact.
- Collaborate with product and engineering teams to integrate AI solutions (e.g., Gemini, OpenAI, Azure AI).
- Stay current on advances in data science, machine learning, and generative AI.
- Mentor junior data scientists and engineers.
- Communicate clearly with stakeholders, presenting insights and recommendations.
- Apply statistical, econometric, and machine learning methods to generate actionable insights and support decision\-making.
- Identify, integrate, and leverage key data sources to enhance analytics.
- Develop scalable data structures and analytic products.
- Lead end\-to\-end analytics projects including data gathering, feature creation, modeling, deployment, and monitoring.
- Provide insights to encourage data\-driven decision\-making throughout the organization.
- Mentor teams on data science best practices and contribute thought leadership through blogs or articles.
- Develop and deliver training on analytics tools and data science techniques (e.g., Alteryx, Tableau).
- Explore and apply new data science tools and methodologies to maintain innovation.
Qualifications:
- Bachelor's degree required; advanced degree (Master’s or Ph.D.) in Data Science, Statistics, Econometrics, Computer Science, or related field strongly preferred.
- 5\+ years of professional experience as a Data Scientist, preferably in a software or SaaS environment.
- Strong programming skills in Python and SQL, including familiarity with advanced analytics libraries and frameworks.
- Experience working within both traditional SDLC and agile development environments.
- Expertise in a wide array of statistical methods, econometrics, machine learning algorithms, artificial intelligence, and predictive analytics.
- Proven experience developing scalable analytic data strategies and robust analytic products.
- Proficiency with visualization tools such as Tableau and experience in business analytics platforms like Alteryx.
- Familiarity with data pipelines, ETL processes, cloud databases, data warehouses (e.g., Snowflake), and data lakes.
- Experience deploying analytics solutions in cloud environments (GCP, AWS \& Azure).
- Strong communication skills, with the ability to clearly articulate complex analytic concepts and results to business stakeholders.
- Excellent problem\-solving and interpersonal skills, capable of independently managing multiple projects in a dynamic environment.
Compensation:
Alteryx is committed to fair, equitable, and transparent compensation. Final compensation is determined by several factors, including but not limited to relevant work experience, education, certifications, skills, and geographic location.
The salary range for this role in the United States is $118,700 \- $153,900\.
Bonus payouts are based on individual and company performance.
In addition to base pay and bonus eligibility, this role includes clear forms of additional compensation, such as:
- A monthly Connectivity Plus stipend of $150 to support remote work\-related expenses
- An annual $200 home office reimbursement
Alteryx offers a comprehensive benefits package designed to support your health, financial security, and overall well\-being, including:
- Medical, dental, and vision coverage
- 401(k) with company match
- Paid parental leave, caregiver leave, and flexible time off
- Mental health support and wellness reimbursement
- Career development and education assistance
*Interested? Learn more and apply today at alteryx.com/careers!*
\#LI\-EM1
\#LI\-REMOTE
Find yourself checking a lot of these boxes but doubting whether you should apply? At Alteryx, we support a growth mindset for our associates through all stages of their careers. If you meet some of the requirements and you share our values, we encourage you to apply. As part of our ongoing commitment to a diverse, equitable, and inclusive workplace, we’re invested in building teams with a wide variety of backgrounds, identities, and experiences .
Benefits \& Perks:
Alteryx has amazing benefits for all Associates which can be viewed here .
For roles in San Francisco and Los Angeles: Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Alteryx will consider for employment qualified applicants with arrest and conviction records.
This position involves access to software/technology that is subject to U.S. export controls. Any job offer made will be contingent upon the applicant’s capacity to serve in compliance with U.S. export controls.
Salary Context
This $118K-$153K range is below the median 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 Alteryx, 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 ($136K) sits 33% below the category median. Disclosed range: $118K to $153K.
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.
Alteryx AI Hiring
Alteryx has 5 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Data Scientist. Positions span CA, US, Remote, US. Compensation range: $153K - $240K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 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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