Go-to-Market Automation Data Scientist, Econometrics

Job Description

Square’s Account Management organization works with Square’s largest and most strategic merchants to grow and retain their businesses and to deliver insights to product teams. Our Go-to-Market Automation team builds automation and machine learning solutions to optimize the efficiency and impact of the Account Management team.

As a Data Scientist on the Go-to-Market Automation team, you will use engineering, analytics, and machine learning to drive insights and decision-making for the Account Management organization. You will partner closely with strategy and business partners to design experimentation and measure lift of different areas within the program. You will leverage insights from these experiments to inform best practices and drive value within Account Management

You will:

• Design and implement measurement methodologies to rigorously evaluate the revenue lift attributable to Account Management programs

• Leverage quasi-experimental techniques to estimate causal parameters using observational data

• Apply descriptive and predictive analytics to help drive insights and business decisions

• Apply a diverse set of tactics such as causal inference, quantitative reasoning, and machine learning to research and produce insights

• Partner closely with cross-functional teams spanning Finance, Strategy, Data Engineering, and Business

• Deeply understand the Account Management data ecosystem, and apply data science to support the growth of the organization

• Communicate analysis and decisions to high-level stakeholders and executives in verbal, visual, and written formats

Qualifications

You have:

• 3+ years of analytics and data science experience or equivalent

• Experience with designing and executing A/B tests and familiarity with a range of lift and impact methodologies

• Experience using quasi-experimental methods (IV, RDD, DiD, etc…) to estimate causal effects using observational data

• Strong written and verbal communication skills and ability to build relationships and influence across the organization

• Proven ability to facilitate cross-functional projects that depend on the contributions of others in a variety of disciplines

• Fluency with data warehouse design practices, analytics, and visualization technologies (we use SQL, Looker, and Python)

Nice to have:

• M.S in a quantitative field (computer science, statistics, economics, or similar STEM field)

• Experience leveraging machine learning techniques in causal inference (DoubleML, Causal Forrest, etc)

• Experience with state of the industry machine learning packages (CausalML, DoWhy)

• Knowledge of Causal DAGs

• Experience leveraging Spark (or other distributed computing framework) to build machine learning models

• Experience applying both statistical and machine-learning techniques to solve practical product problems such as predicting churn, LTV, cross-selling, connect rate

Additional Information