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About This Role
Bevi is on a mission to transform how beverages are delivered and consumed. Our connected beverage platform eliminates the need for single\-use bottles and cans, making it easy, fun, and sustainable to stay hydrated. As the category leader in IoT\-enabled beverage technology, we're building a future where Bevi machines are everywhere people live, work, and connect. We've raised over $160M in venture capital, serve thousands of customers across the US, Canada, UK and Ireland, and we've been rapidly growing year over year, saving over 1 billion bottles from waste. In addition to driving hypergrowth with our current product line, Bevi is heavily investing in new product development.
Ever wonder which customers are about to churn before they do, or whether that marketing campaign actually moved the needle instead of just riding a coincidence? That's this role. Bevi's go\-to\-market (GTM) strategy is built at the intersection of Marketing and Sales: how we acquire new customers, and how we retain and grow existing accounts. We're looking for a GTM Data Scientist who can accelerate this function. On the marketing side, you'll measure the true incremental impact of our marketing spend across the full funnel \- digital and offline alike, from paid social to events to BeviMobile \- and build the leading indicators that keep the team grounded in what's working between deeper reads. On the customer side, you'll build the models that flag churn risk, surface expansion and upgrade opportunities, and identify look\-alike prospects. You'll partner closely with Sales, Marketing, and RevOps stakeholders to turn data into a clear point of view on what to do next. We're looking for someone who spots what needs to get built before being asked, and drives it to done rather than waiting for direction.
Your Day to Day:
- Build predictive models to identify churn risk and surface upgrade/expansion opportunity across our customer base to inform proactive outreach and account prioritization.
- Build look\-alike models to identify which prospects resemble our best customers, and own cohort reporting to track how customer segments perform over time.
- Build marketing mix models (MMM) and incrementality analyses to measure the true impact of marketing spend across the full funnel \- digital and offline, including events, BeviMobile, and social \- and inform marketing budget optimization decisions.
- Identify leading indicators \- including proprietary composite metrics \- for weekly reporting that give early signal on marketing performance in between full MMM reads.
- Partner with the Marketing Analytics Engineer to inform the data structures your modeling work needs, and ensure the underlying data is accurate and well understood.
- Translate data and analytics into clear insights and recommendations.
Who You Are:
- 2\-4 years of professional experience in data science, applied statistics, or analytics, ideally with exposure to customer/revenue analytics or marketing measurement.
- Hands\-on experience building predictive/classification models (e.g., churn, propensity, look\-alike) using techniques like logistic regression, gradient boosting, or similar.
- Experience with causal inference or marketing measurement methods (e.g., MMM, incrementality testing, difference\-in\-differences) \- comfortable incorporating both digital and offline channels (eg events, experiential) into your models, not just clean digital data.
- Strong SQL and Python/R for querying, modeling, and analysis.
- Experience with data visualization tools (e.g., Looker, PowerBI, Hex).
- A creative problem solver, comfortable designing a measurement approach when the textbook experiment isn't available.
- You use AI tools in your own workflow to move faster (e.g., exploratory analysis, code, documentation), and think about how to make your models and analyses accessible to AI tools as well as people.
- A proactive, go\-getter mindset \- you notice what needs to get built before you're asked, and drive your own work to completion without needing to be chased.
- Excellent communication skills \- able to translate complex findings into clear, actionable recommendations for non\-technical stakeholders.
\#LI\-CD1
\#LI\-HYBRID
#### Benefits:
- Comprehensive medical, dental and vision insurance plans with BlueCross BlueShield, 95% paid by employer
- 401(k) with company match
- Flexible PTO plus 12 company holidays, and additional paid days for sick leave, etc
- Generous fully paid parental leave for both birth parents and non\-birth parents
- Fully employer paid disability and life insurances
- Wellness and fitness reimbursements
- Monthly stipends for cell phone use and commuting costs
- Onsite snacks, weekly catered lunch, and (of course) unlimited Bevi ... plus composting and terra\-cycling, too
- Happy hours, team\-building events, bagel breakfasts, Values awards \- and more.
A note on AI in our hiring process: We use AI to help synthesize interview notes and surface themes from feedback, keeping our process consistent as we grow. Every application is reviewed by a member of our team, and interviews may be recorded to support note\-taking, just let us know if you'd prefer we don't. We'll also ask how you're learning and experimenting with AI in your work. Growing our AI fluency is something every Bevi team member is committed to and invested in.
Our commitment to responsible innovation is reflected in our partnership with the Mass AI Coalition, a collaboration that aligns with our dedication to community\-driven progress and the future of technology.
*We're passionate about supporting career growth and would love to be part of your professional journey. We also know that talent comes in many forms. We value individual accomplishments, specialized knowledge, and genuine passion over simply checking boxes on a requirements list. Whether you're a seasoned expert or an emerging talent, we're looking for people who are ready to build what's next. If any of our open positions interest you, we'd encourage you to apply!* *A member of the Bevi Talent team '[email protected]' will be reaching out about next steps if we would like to move forward.*
#### Accommodations:
Bevi is committed to an inclusive hiring process and we aim to provide accommodations for persons with disabilities. If you need any accommodations for the application or throughout the interview process please contact [email protected].
Salary Context
This $120K-$149K range is below the median 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 Bevi, 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 ($134K) sits 30% below the category median. Disclosed range: $120K to $149K.
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.
Bevi AI Hiring
Bevi has 1 open AI role right now. They're hiring across Data Scientist. Based in Boston, MA, US. Compensation range: $149K - $149K.
Location Context
AI roles in Boston pay a median of $210,000 across 166 tracked positions.
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.
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