Senior Data Scientist

Introduction to Demandbase: 

Demandbase is the leading account-based GTM platform for B2B enterprises to identify and target the right customers, at the right time, with the right message. With a unified view of intent data, AI-powered insights, and prescriptive actions, go-to-market teams can seamlessly align and execute with confidence. Thousands of businesses depend on Demandbase to maximize revenue, minimize waste, and consolidate their data and technology stacks - all in one platform.

As a company, we’re as committed to growing careers as we are to building world-class technology. We invest heavily in people, our culture, and the community around us. We have offices in the San Francisco Bay Area, Seattle, and India, as well as a team in the UK, and allow employees to work remotely. We have also been continuously recognized as one of the best places to work in the San Francisco Bay Area including, “Best Workplaces for Millennials” and “Best Workplaces for Parents”!

We're committed to attracting, developing, retaining, and promoting a diverse workforce. By ensuring that every Demandbase employee is able to bring a diversity of talents to work, we're increasingly capable of living out our mission to transform how B2B goes to market. We encourage people from historically underrepresented backgrounds and all walks of life to apply. Come grow with us at Demandbase!

About the Role:

Demandbase is seeking a Senior Data Scientist to join our ML/Data Science team, where you’ll contribute to impactful machine learning projects that shape the B2B buyer journey. This role is ideal for someone with solid experience in casual inference including experimentation (A/B testing) and observational methods, statistical modeling, machine learning methods, time-series analysis, etc. You will work on a range of projects including account ranking, recommendation systems, intent modeling, and ads optimization. You will also work on setting up experiments (A/B testing) and apply other causal inference methods to measure and compare model performance and assess the impact of the AI/ML tools our team builds.

In this role, you’ll collaborate with experienced Data Scientists, Machine Learning Engineers, Applied Scientists, Product Managers, Data Engineers, Software Engineers, Designers, UX Researchers, etc. offering opportunities to apply your casual inference experience and machine learning skills. A background in ML/Data Science, Econometrics, Statistics, Computer Science, or a related field is ideal.

The base compensation range for this position for candidates in the SF Bay Area is: $204,000 - $266,000. For all other locations, the base compensation range is based on the primary work location of the candidate as our ranges are location specific. Actual compensation packages are based on a wide array of factors unique to each candidate, including but not limited to skillset, years of experience, and depth of experience.

This position is based in San Francisco, CA. Remote will be considered for highly qualified candidates.

What you’ll be doing:

• Measure Impact and ROI: Apply causal inference methods including experimentation (A/B testing) as well as observational methods (propensity score matching, difference-in-differences, synthetic controls, etc.) to measure the performance of our models and the impact our models have for our customers.
• Data Exploration, Feature Engineering, and Model Building: Analyze large datasets to support machine learning models, perform feature engineering, and develop and apply machine learning models to our backend and frontend applications.
• Model Performance and Validation: Evaluate and validate model performance, using metrics to improve accuracy and reliability in production environments.
• Cross-functional Collaboration: Work with data engineering, product, software engineering, and UX teams to integrate models into workflows, ensuring solutions align with business needs.
• Communication of Findings: Present analysis and model results to technical and non-technical stakeholders, using data visualizations and clear explanations to convey insights effectively.

Project Highlights

• Ad Optimization and Personalization: Build models and measure their impact in the space of optimizing ad campaigns and real-time bidding.
• Account Intelligence & Scoring: Use ML to rank and prioritize accounts within the B2B buying journey.
• Real-Time Intent Detection: Develop models to capture intent signals that drive real-time sales and marketing actions.

• Support Experiments: Set up and support