Outreach

About the Team:

Data is at the core of Outreach's strategy. It drives ourselves and our customers to the highest levels of success. We use it for everything from customer health scores and revenue dashboards to operational metrics of our AWS infrastructure, to helping increase product engagement and user productivity through natural language understanding, to predictive analytics and causal inference via experimentation. As our customer base continues to grow, we are looking towards new ways of leveraging our data to deeper understand our customers’ needs and deliver new products and features to help continuously improve their customer engagement workflows.

The mission of the Data Science team is to enable such continuous optimization by reconstructing customer engagement workflows from data, developing metrics to measure the success and efficiency of these workflows, and providing tools to support the optimization of these workflows.

As a member of the team, you will work closely with other data scientists, machine learning engineers, and application engineers to define and implement our strategy for delivering on this mission

Your Daily Adventures Will Include:

• Design, implement, and improve machine learning Systems.
• Contribute to machine learning applications end to end, i.e. from research to prototype to production.
• Work with product managers, designers, and customers to define vision and strategy for a given product.

Our Vision of You:

• A hybrid data science engineer who can navigate both sides with little help from others
• You understand the typical lifecycle of machine learning product development, from inception to production. Experience in Gen AI application/agent development is a plus.
• You have strong programming skills in at least one programming language (Python, Golang, etc.). Experience in framework like Langchain, OpenAI Agent SDK is a plus.
• You have experience building microservices. Experience with Golang is a plus.
• You have substantial experience with building and managing infrastructure for deploying and running ML models in production
• You have experience working with distributed data processing frameworks such as Spark. Experience with Spark's MLlib, AWS, Databricks, MLFlow are a plus
• You have a knowledge in statistics and machine learning and have practical experience applying it to solve real-world problems.
• You are hands-on, able to quickly pick up new tools and languages, and excited about building things and experimenting.
• You go above and beyond to help your team
• You should be able to work alongside experienced engineers, designers, and product managers to help deliver new customer-facing features and products.
• You have an degree in Computer Science, Data Science, or a related field, and 4-6 years of industry or equivalent experience