Senior Applied Scientist, Demandbase

• 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
• A background in ML/Data Science, Econometrics, Statistics, Computer Science, or a related field is ideal
• Experience: 5+ years in data science or applied machine learning roles, with demonstrated experience in causal inference and building and deploying models
• Education: Graduate degree (M.S. or Ph.D.) in Statistics, Econometrics, Computer Science, Machine Learning, Mathematics or a related field
• Casual Inference and ML Expertise: Proven experience applying casual inference methods (experimental and observational) as well as ML methods including:
• Experimentation (A/B testing)
• Observation Studies (propensity score matching, difference-in-differences, synthetic controls, etc.)
• Statistical modeling, ML methods
• Ranking & Recommendation Systems
• Experience with ad optimization and real-time bidding algorithms (preferred)
• Experience with experimentation and causal inference packages such as EconML, CausalImpact
• Strong proficiency in Python and ML libraries (e.g., Scikit-Learn, Pandas, Numpy)
• Solid SQL skills
• Familiarity with ML frameworks such as PyTorch and TensorFlow
• Experience with Scala is a plus
• Cloud & Data Tool Familiarity: Experience with Google Cloud Platform or AWS preferred, with BQML or Spark as a plus

Desirable

• Demandbase is seeking a Senior Applied Scientist to join our ML/Data Science team, where you’ll contribute to impactful machine learning projects that shape the B2B buyer journey
• 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
• 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
• 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 backend and front-end A/B tests to measure performance and support model development and rollout decisions