Senior Applied Scientist

About the Role

Key Responsibilities

• Apply causal inference methods including experimentation (A/B testing) and observational methods (propensity score matching, difference-in-differences, synthetic controls, etc.) to measure the performance of models and their impact for customers.
• Analyze large datasets to support machine learning models, perform feature engineering, and develop and apply machine learning models to backend and frontend applications.
• Evaluate and validate model performance, using metrics to improve accuracy and reliability in production environments.
• Work with data engineering, product, software engineering, and UX teams to integrate models into workflows, ensuring solutions align with business needs.
• Present analysis and model results to technical and non-technical stakeholders using data visualizations and clear explanations.
• Build models and measure their impact in ad optimization and real-time bidding.
• Use ML to rank and prioritize accounts within the B2B buying journey.
• Develop models to capture intent signals that drive real-time sales and marketing actions.
• Set up and support backend and front-end A/B tests to measure performance and support model development and rollout decisions.

Requirements

• 8+ years in data science or applied machine learning roles, with demonstrated experience in causal inference and building and deploying models.
• Graduate degree (M.S. or Ph.D.) in Statistics, Econometrics, Computer Science, Machine Learning, Mathematics or a related field.
• Proven experience applying causal 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.).
• Experience with statistical modeling and ML methods, ranking & recommendation systems, ad optimization, and real-time bidding algorithms (preferred).
• Technical skills including experience with experimentation and causal inference packages such as EconML, CausalImpact, proficiency in Python and ML libraries (e.g., Scikit-Learn, Pandas, Numpy), solid SQL skills, and familiarity with ML frameworks like PyTorch and TensorFlow.
• Experience with Scala is a plus.
• Cloud & data tools experience with Google Cloud Platform or AWS preferred, with BQML or Spark as a plus.
• Strong proficiency in Python and ML libraries, experience with experimentation and causal inference packages, and familiarity with cloud platforms.

Nice to Have

• Leadership experience in leading projects or mentoring within data science teams.
• Prior work experience in Martech, SalesTech, or Adtech environments.
• Experience with Scala.
• Experience with Google Cloud Platform or AWS, BQML, or Spark.

Qualifications

• Graduate degree (M.S. or Ph.D.) in Statistics, Econometrics, Computer Science, Machine Learning, Mathematics or a related field.
• 8+ years of experience in data science or applied machine learning roles.

Benefits & Perks

• Up to 100% paid premiums for Medical and Vision coverage.
• Access to mental wellness resources including Modern Health.
• Flexible PTO policy and 15 paid holidays in 2025, including a three-day break around July 4th and a full week off for Thanksgiving.
• No Internal Meetings Fridays.
• Competitive 401(k) plan, short-term and long-term disability coverage, life insurance, and other benefits.