Manager, Sales Engineering, Central US and Canada

Dataiku is the platform for Everyday AI, systemizing the use of data for exceptional business results. At its core, Dataiku allows companies to leverage one central solution to design, deploy, and manage AI and analytics applications, and it’s accessible for everyone (whether technical and working in code or on the business side and low- or no-code). We seek an energetic, optimistic, curious, empathic coach and leader to manage a growing team of Enterprise Sales Engineers in the Central US and Canada.

• Lead a team of trusted advisors who are responsible for demonstrating the value of Dataiku’s product
• Partner with the sales organization to earn new and retain existing Dataiku clients
• Help prospective customers understand how Dataiku can help solve business challenges
• Collaborate with internal stakeholders across product, marketing, sales, customer success, and others
• Mentor your team; hire and retain top talent

• Coach sales engineers on Dataiku’s product value, and the application of the Challenger Sales methodology
• Serve as a player/coach to engage on high-profile customer opportunities
• Serve as a technical point of contact for sales leadership; communicate technical risks and opportunities faced in sales engagements
• Mentor team members; host regular check-ins, provide career guidance, and hire and scale team
• Build strong working relationships with cross functional teams to ensure alignment 
• Communicate directly with sales leadership to strategize
• Host enablement sessions
• Partner with the Sales Engineering leadership to refine key performance indicators for your team

• At least five years of experience in sales engineering
• At least two years of people management experience, including a passion for building and hiring teams
• Experience in complex / large-scale enterprise analytics deployment
• Experience building and enabling high performing teams
• Demonstrated  intellectual curiosity regarding the intersection of business and technology

• Proficiency in programming languages (Python, R, SQL, Java, etc..) 
• Experience with cloud services (AWS, Azure, GCP) 
• Understanding of the machine learning lifecycle (feature engineering, model training, model deployment, batch and real time scoring) and engineering considerations 
• Hands on experience with computational resources and frameworks (containerization, Kubernetes, Spark)
• Knowledge of machine learning frameworks such as TensorFlow, PyTorch or Scikit-learn
• Knowledge of Kubeflow, Airflow, or other orchestration tools
• Excellent communication and presentation skills