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 modeling customer engagement workflows, developing metrics to measure success and efficiency of these workflows and providing insights and tools to support execution and optimization of these workflows. As a member of the team, you will be on the ground floor working with Product Managers, Data Scientists, Machine Learning Engineers, Application Engineers and executives to define and implement our strategy for delivering on this mission.
As a manager of the Sales Process Optimization team you will be responsible for defining and implementing algorithms and engineering platforms for automated optimization of sales workflows, aka sales playbooks. For most organizations, their sales playbooks are highly suboptimal, defined based on intuition and personal experience of sales leaders, without much data analysis involved. Your goal will be to change that. You will build and manage a cross-functional team consisting of data scientists, machine learning engineers and application engineers working on transforming static intuition-based sales playbooks into dynamically optimized sales processes. Your goal will be to not just build features and systems, but to forever revolutionize how sales works.
Our Vision of You
- You have 10+ years of technical experience including;
- Application development including building backend and front-end for distributed data/ML-based applications
- Building production data pipelines with distributed data processing frameworks such as Spark.
- Experiences with Spark’s Mlib, AWS, Databricks, MLFlow are a plus.
- Deploying and running statistical and ML models in production
- You are familiar with A/B testing, Reinforcement Learning and NLP, and have practical experience in at least one of these areas.
- You understand the entire lifecycle of statistical and machine learning product development, from inception to production
- You have substantial coding experience. Experience with Python and its ML packages such asscikit-learn is a plus.
- You are hands on, able to quickly pick up new tools and languages, and excited about building things and experimenting
- You are experienced at mentoring team members to help them learn and grow beyond what they thought was possible, both technically and personally. You go above and beyond to make your team successful
Why You’ll Love It Here
• Generous medical, dental, and vision coverage for full-time employees and their dependents
• Flexible time off
• 401k to help you save for the future
• Company-organized and personal paid volunteer days to support the community that supports us
• Fun company and team outings (or virtual events these days!) because we play just as hard as we work
• Diversity and inclusion programs that promote employee resource groups like OWN (Outreach Women's Network), AAPI, Rainbow (LGBTQIA+), Gender+, LatinX, Black Excellence, Disability Community, and Veterans
• A parental leave program that includes not just extended time off but options for a paid night nurse, food delivery, gradual return to work, and the Gottman Institute's Bringing Home Baby course for new parents
• Employee referral bonuses to encourage the addition of great new people to the team
• Plus, unlimited snacks and beverages in our kitchen (once we're back in the office, that is!)
• We’re an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status