Senior Data Analyst, Revenue Analytics

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.

Please take a look at remote.com/handbook to learn more about our culture and what it is like to work here. Not only do we encourage folks from all ethnic groups, genders, sexuality, age and abilities to apply, but we prioritize a sense of belonging. You can check out independent reviews by other candidates on Glassdoor or look up the results of our candidate surveys to see how others feel about working and interviewing here.

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

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 spearhead the advanced analytics capability at Remote, building predictive models within Sales & Marketing, generating insights, 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). As the first Data Scientist, the person has a unique 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

• At least 2-3 years of 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 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)
• An attitude to “get stuff done” particularly around being actively involved with data cleaning, 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 Tableau/Looker, dbt, Advanced university degree in Statistics/Mathematics/Physics or other quantitative field
• 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 6-9 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 technically sound but highly effective and widely useful
• Building predictive models around different business areas and problems within Revenue Analytics : Lead Scoring, Marketing Attribution, Customer Churn Prediction, Optimizing Sales Processes
• Maintaining the data pipelines and statistical models, and also continuously calibrate existing models to account for changing business conditions and/or customer behaviour
• Using causal inference and quasi-experimental techniques to dig into historical & current data to statistically identify which initiatives worked and what was the lift they produced
• Owning the data science domain technically, creating new knowledge as well as raising the standards for advanced analytics work

Practicals

• You'll report to: Director of Revenue Analytics
• Direct reports: None
• Team: RevOps - GTM Analytics
• Location: For this position we welcome everyone to apply, but we will prioritise applications from EMEA as we encourage our teams to diversify.
• Start date: As soon as possible

Remote Compensation Philosophy

Remote's Total Rewards philosophy is to ensure fair, unbiased compensation and fair equity pay along with competitive benefits in all locations in which we operate. We do not agree to or encourage cheap-labor practices and therefore we ensure to pay above in-location rates. We hope to inspire other companies to support global talent-hiring and bring local wealth to developing countries.

At first glance our salary bands seem quite wide - here is some context. At Remote we have international operations and a globally distributed workforce. We use geo ranges to consider geographic pay differentials as part of our global compensation strategy to remain competitive in various markets while we hiring globally.

The base salary range for this full-time position is between $35,850 USD to $120,950 USD. Our salary ranges are determined by role, level and location, and our job titles may span more than one career level. The actual base pay for the successful candidate in this role is dependent upon many factors such as location, transferable or job-related skills, work experience, relevant training, business needs, and market demands. The base salary range may be subject to change.

Application process

Roughly 4 hours across 4 weeks

1. Interview with Recruiter (30-45 mins)
2. Interview & Live Technical Assessment with future manager (90 mins)
3. Interview with Team Members (no managers present) (45 mins)
4. Executive Interview (30 minutes)
5. Prior employment verification check

Benefits

• work from anywhere
• unlimited personal time off (minimum 4 weeks)
• quarterly company-wide day off for self care
• flexible working hours (we are async)
• 16 weeks paid parental leave
• mental health support services
• stock options
• learning budget
• home office budget & IT equipment
• budget for local in-person social events or co-working spaces

How you’ll plan your day (and life)

We work async at Remote which means you can plan your schedule around your life (and not around meetings). Read more at remote.com/async.

You will be empowered to take ownership and be proactive. When in doubt you will default to action instead of waiting. Your life-work balance is important and you will be encouraged to put yourself and your family first, and fit work around your needs.

If that sounds like something you want, apply now!

How to apply

1. Please fill out the form below and upload your CV with a PDF format.
2. We kindly ask you to submit your application and CV in English, as this is the standardised language we use here at Remote.
3. If you don’t have an up to date CV but you are still interested in talking to us, please feel free to add a copy of your LinkedIn profile instead.

We will ask you to voluntarily tell us your pronouns at interview stage, and you will have the option to answer our anonymous demographic questionnaire when you apply below. As an equal employment opportunity employer it’s important to us that our workforce reflects people of all backgrounds, identities, and experiences and this data will help us to stay accountable. We thank you for providing this data, if you chose to.