Airtable is looking to accelerate the efficiency of Customer Engagement (CE) organizations including sales, CSG and revenue operations teams by building high-quality and customer-centric data products and solutions.
Requirements
6+ years of experience working with SQL in modern data platforms, such as Databricks, Snowflake, Redshift, BigQuery
6+ years of experience working with Python or R for analytics or data science projects
6+ years of experience building business facing dashboards and data models using modern BI tools like Looker, Tableau, etc.
Proficient-level experience developing automated solutions to collect, transform, and clean data from various sources, by using tools such as dbt, Fivetran
Proficient knowledge of data science models, such as regression, classification, clustering, time series analysis, and experiment design
Hands-on experience with batch LLM pipeline is preferred
Familiar with CRM platforms (i.e., Salesforce)
Responsibilities
Champion AI Driven Data Product with Scalability: Design and implement ML models and AI solutions to enable CE team with actionable insights and recommendations. Build scalable data pipelines and automated workflows with MLOps best practices.
Support Key Business Processes: Provide strategic insights, repeatable frameworks and thought partnership independently to support key CE business processes like territory carving, annual planning, pricing optimization and performance attribution, etc..
Strategic Analysis: Drive in-depth deep-dive analysis to ensure accuracy and relevance. Influence the business stakeholders with a good story telling of the data. Tackle ambiguous problems to uncover business value with minimal oversight.
Develop Executive Dashboards: Design, build, and maintain high-quality dashboards and BI tools. Partner with Revenue Operations team to enable vast roles of CE team efficiently with the data products.
Strong Communication Skills: Effectively communicate the “so-what” of an analysis, illustrating how insights can be leveraged to drive business impact across the organization.
Other
Education: Bachelor degree in a quantitative discipline (Math, Statistics, Operations Research, Economics, Engineering, or CS), MS/MBA preferred.
Industry Experience: 4+ years of working experience as a data scientist / analytics engineer in high-growth B2B SaaS, preferably supporting sales, CSG or other go-to-market stakeholders.
Demonstrated business acumen with a deep understanding of Enterprise Sales strategies (sales pipeline, forecast models, sales capacity, sales segmentation, quota planning), CSG strategies (customer churn risk model, performance attribution) and Enterprise financial metrics (ACV, ARR, NDR)
Excellent communication skills to present findings to both technical and non-technical audiences.
Passionate to thrive in a dynamic environment. That means being flexible and willing to jump in and do whatever it takes to be successful.
