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About This Role
About the job
Company: Persist Brands
Job Title: Data Scientist
Location: Remote
Employment Type: Full\-time/ Contract
Compensation: Range provided is purely variable based on experience and employment type and multiple other business factors
Reporting to: CFO
About Persist Brands
Persist Brands is a fast\-growing direct\-to\-consumer eCommerce company that builds and scales aggressive direct\-response consumer brands across categories like apparel, beauty, wellness, and lifestyle. We combine performance marketing, creative testing, data analysis, and operational execution to turn consumer products into scalable offers.
We are a performance\-driven company built around speed, data, creative testing, and operational execution. Our team launches and scales products through paid media, high\-converting funnels, strong creative strategy, customer retention, and disciplined backend operations.
At Persist Brands, we move fast, test aggressively, and optimize constantly. We are looking for ambitious, resourceful, and highly accountable people who want to join a lean, growth\-focused company where their work has a direct impact on revenue, customer experience, and long\-term brand value.
The Role
The Marketing Data Scientist will leverage advanced analytics, statistical modeling, machine learning, and data\-driven insights to optimize marketing performance, improve customer acquisition, enhance retention strategies, and maximize revenue growth. This role partners closely with Marketing, Growth, Product, and Business teams to translate complex data into actionable strategies that drive measurable business outcomes.
The ideal candidate combines strong analytical skills with marketing expertise and has experience working with customer data, digital marketing channels, attribution models, experimentation, and predictive analytics.
What You'll Do:
- Analyze marketing, customer, sales, and campaign data to identify trends and growth opportunities.
- Track and analyze key marketing metrics such as CAC, ROAS, conversion rate, revenue, retention, and LTV.
- Build and maintain marketing performance reports, dashboards, and recurring analysis.
- Use SQL and Python to extract, clean, analyze, and visualize data.
- Conduct customer segmentation, cohort analysis, funnel analysis, and behavioral analysis.
- Support A/B testing and marketing experiments, including test setup, analysis, and interpretation of results.
- Identify patterns and performance drivers across marketing channels and customer segments.
- Support forecasting and basic predictive analysis where required.
- Work with Marketing, Growth, and Operations teams to answer business questions using data.
- Communicate findings clearly and provide practical recommendations to improve marketing performance.
- Ensure data accuracy and consistency across reports and analysis.
What We're Looking For:
- Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Economics, or a related quantitative field.
- 2–3 years of experience in Data Science, Marketing Analytics, Business Analytics, or a similar role.
- Strong proficiency in SQL.
- Working knowledge of Python or R for data analysis.
- Strong understanding of statistics and data analysis.
- Experience with A/B testing, segmentation, cohort analysis, and performance reporting.
- Ability to work with large datasets and identify meaningful business insights.
- Strong problem\-solving and communication skills.
Deal\-Breakers/ Must haves:\-
- Ecommerce, DTC, subscription, or performance marketing experience.
- Experience with Google Ads, Meta Ads, GA4, Shopify, CRM, and marketing analytics platforms.
- Experience with BI tools such as Tableau, Looker, or Power BI.
- Experience with customer segmentation, cohort analysis, attribution, and marketing mix analysis.
- Reduction in reporting and analysis turnaround time.
- Data accuracy and consistency across marketing reports.
- Adoption of insights and recommendations by Marketing and Growth teams.
Note:\- Pay range provided is for Full\-time Employment. If you are a Global candidate and being interviewed for a Contract position, pay range will vary based on region, experience and other business factors. Do not consider same pay range for both Employment types.
Pay: $70,000\.00 \- $90,000\.00 per year
Application Question(s):
- Tell me about your marketing/e\-commerce analytics experience.
- What BI/dashboarding tools have you used?
- Explain how you would design an A/B test for a marketing campaign.
- Campaign performance dropped 30%. How would you investigate it?
- What marketing channels and datasets have you worked with?
- Do you have eCommerce industry experience?
- What would you do if marketing and finance report different revenue numbers?
- Tell me about an analysis that resulted in a business or marketing decision.
Work Location: Remote
Salary Context
This $70K-$90K range is in the lower quartile for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Persist Brands, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($80K) sits 59% below the category median. Disclosed range: $70K to $90K.
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.
Persist Brands AI Hiring
Persist Brands has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $90K - $90K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 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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