About Remote

Remote is solving global remote organizations’ biggest challenge: employing anyone anywhere compliantly. We make it possible for businesses big and small to employ a global team by handling global payroll, benefits, taxes, and compliance. Check out remote.com/how-it-works to learn more or if you’re interested in adding to the mission, scroll down to apply now.

Not only do we encourage folks from all ethnic groups, genders, sexuality, age, abilities, disability status and any other under-represented group to apply, but we prioritize a sense of belonging. We have 4 ERGs (Women, Disability, Queer, Minorities in Tech) who meet regularly with the People team. During your interviews and beyond, we ask & encourage anybody who needs an accommodation to request one from their recruiter.

At Remote, we embrace AI as a valuable tool while prioritizing human creativity and authenticity. We look forward to meeting candidates who balance innovation with genuine expertise and experience. To learn more about Remote's AI guidelines check see here. 

All of our positions are fully remote. You do not have to relocate to join us!

What this job can offer you

Senior Data Analyst, Revenue Analytics is a crucial hire not only for our Revenue Analytics team, but for Remote more broadly. This individual will help move the advanced analytics capabilities at Remote, generating insights, building predictive models within Sales & Marketing, and test hypotheses around what drives revenue growth. In addition, there will be a traditional Reporting & Analytics component to the role (so, it will be a roughly 50% Data Scientist & 50% Data Analyst split in responsibilities in practice). With our growing analytics team and a modern tech stack, the person has an opportunity to bring advanced craft to Remote, develop both new solutions and ways of looking at existing processes, and raise the standards of what is possible using data and machine learning across the entire organization. Depending on candidate qualifications, expertise and desires, this role can develop into different areas in the future.

What you bring

• Experience in data science / advanced analytics / statistical modelling experience, ideally within Sales/Marketing/Finance or other commercial parts of the organization
• Working knowledge of workhorse machine learning algorithms (particularly supervised learning using Python), and their typical use cases in a commercial organization
• Insatiable curiosity to proactively pose deeper questions, not be satisfied with superficial answers, and dig into vast granular data to uncover conclusive answers analytically
• Strong proficiency in SQL (in Snowflake) to manipulate data easily
• Demonstrated ability to craft precise analytical questions from ambiguous business problems, and roll up the sleeves to address them
• Understanding of causal inference methods to be able to utilize quasi-experimental or other econometric techniques to determine causality among different initiatives (essentially, finding out “what works,” and what does not, especially around Sales & Marketing questions)
• An attitude to “get stuff done” particularly around being actively involved with data cleaning and quality, building reports or answering important business questions (being a full-stack data professional)
• Top-Tier communication skills in English, to distill complex mathematical models, concepts and findings into simple, intuitive words and charts for senior commercial leaders

Nice To Have:

• Startup/Scaleup Experience, AWS Knowledge, Comfort with distributed & remote teams, EMEA Location, SalesForce Data Experience, Hands-On familiarity with a BI Tool such as Sigma/Looker, dbt, Advanced university degree in Statistics/Mathematics/Physics or other quantitative field
• Go-To-Market (GTM)/Sales Analytics Experience a huge plus
• Some experience of deploying ML models into production ideally highly meriting
• It's not required to have experience working remotely, but considered a plus

 

Key Responsibilities

• Initially focusing on developing and maintaining KPIs and dashboards with the rest of the team, to monitor metrics and uncover insights as the primary responsibility for the first ~3-6 months (also to understand the business effectively)
• Liaising productively with Data Analysts, Analytics Engineers and the business teams/owners to ensure that data products, analysis & statistical models built are not only technical