Data Scientist - Forecasting & Analytics

About Gusto

Gusto is a modern, online people platform that helps small businesses take care of their teams. On top of full-service payroll, Gusto offers health insurance, 401(k)s, expert HR, and team management tools. Today, Gusto offices in Denver, San Francisco, and New York serve more than 200,000 businesses nationwide.

Our mission is to create a world where work empowers a better life, and it starts right here at Gusto. That’s why we’re committed to building a collaborative and inclusive workplace, both physically and virtually. Learn more about our Total Rewards philosophy.

The Role:

Gusto is looking for a Data Scientist in our Revenue Operations analytics team. You will work cross-functionally with Data Science, Marketing and Sales to develop forecasts, and provide best-in-class revenue-impacting techniques that power the F&A activities. 

It is expected that advanced methods, such as AI/ML, should form the core of our technological approach on par with industry-leading RevOps tools and platforms. 

You will help build and develop ML/AI models that could enhance the Integrated RevOps Forecasting pipeline and provide next-best action recommendations capable of scaling to personalized lifecycle interventions. The role is to own and build these products with the support of immediate team members to build data models and assist in business-related requirements gathering etc, whilst working in cross-functional partnerships (e.g. with MarOps, SalesOps, marketing, DS) to deliver revenue-impacting results.

You will also have sufficient acumen to know how to implement the various checks and balances to ensure the highest quality of forecast data, including fallback methods for when data sources drift.

Compensation: Our cash compensation amount for this role is targeted at $134,000 - $153,000/year in Denver & most remote locations, and $162,000 - $177,500/year for San Francisco & New York. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed above.

Here’s what you’ll do day-to-day:

• Develop statistical and machine learning models on large and noisy datasets to build robust revenue forecasting methodologies and provide diagnostic insights into the causes of data trends and anomalies. 
• Support RevOps analytics activities by conducting statistical analysis and hypothesis testings leveraging multiple data sources 
• Apply latest advances in causal inference (particularly on time series data) to provide ad-hoc causal analysis of interventions and next-best actions frameworks. 
• Work closely with data scientists to evaluate and implement a range of supporting models, such as LTV predictions, Propensity Models, causal analysis, etc.
• Adopt best practices to develop, implement, and manage models and algorithms that scale our forecasting operations
• Collaborate with data platform engineers to productize models and algorithms as appropriate, and monitor model performance in the field

Here’s what we're looking for:

• Bachelor's Degree in Statistics, Engineering, Applied Mathematics, Computer Science, or a related field. Master’s preferred.
• 2/3+ years experience applying advanced statistical and data science techniques
• Have a background in statistics and understand different distributions and the conditions under which they're valid.
• Possess a strong fundamental understanding of machine learning algorithms (e.g. regressions, decision trees, k-means clustering, neural networks)
• Significant experience working with time series data is preferred. Prior experience with Python-based libraries for time series, such as Prophet, and deep learning architectures like 1D Convolutional Neural Networks will be a plus!
• Experience applying causal inference techniques.
• Understanding of frequentist and Bayesian statistics.
• Proficient in scientific Python (NumPy, SciPy, scikit-learn, Pandas), SQL, and version control (GitHub). 
• Demonstrable experience in full stack data science, including developing and deploying machine learning models in a production environment (Airflow preferred).
• Ability to formulate hypotheses, draw conclusions and deliver results
• Excellent verbal and written communication skills
• Ability to present findings to stakeholders and communicate results to technical and non-technical audiences.

Our customers come from all walks of life and so do we. We hire great people from a wide variety of backgrounds, not just because it's the right thing to do, but because it makes our company stronger. If you share our values and our enthusiasm for small businesses, you will find a home with us. 

Our company is proud to be an equal opportunity employer. We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or other applicable legally protected characteristic. Our company considers qualified applicants with criminal histories, consistent with applicable federal, state and local law. We are also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, you may contact us at [email protected].