Head of Machine Learning Job Description Template

As the Head of Machine Learning, you will develop and lead a team of brilliant machine learning engineers and data scientists to imaginatively brainstorm new data sources, pipelines, algorithmic approaches, and strategies to enhance and expand upon our trend prediction and demand forecasting infrastructure. You will develop cutting-edge computational paradigms for computer vision, sentiment analysis, and recommendation systems while working with a brilliant team of operators and marketers to optimize our choices for what to design, how much to produce, which up-and-coming social media influencers to collaborate with, and how to graduate yet fully personalize the customer journey. 

Typical Duties and Responsibilities

• Lead and manage a team of machine learning scientists and engineers
• Collaborate with the engineering team to make pivotal decisions regarding infrastructure, architecture, and scalability
• Work with the product team to incorporate their aims with the machine learning strategy and development
• Communicate deep scientific concepts in understandable language for internal teams and clients
• Create systems that benchmark advancement, prevent regressions, and are simple to manage while embracing the challenges of big data, performance, and scalability
• Work with other company leaders to decide on strategies and roadmaps
• Maintain and scale existing technologies
• Stay informed of the latest science, biology, medicine, and machine learning developments
• Serve on the executive committee to align technology goals to the organizational objectives

Education

• Bachelor’s degree in computer science, data science, mathematics or a related field

Required Skills and Experience

• 2+ years of experience as a machine learning engineer
• Experience working with and managing teams using complex data and ML
• Experience working with and scaling up enormous datasets
• Experience leading engineering and data teams that productionize big data and machine learning solutions
• Extensive knowledge of many different aspects of machine learning, including general ML, network analysis, time series, reinforcement learning, and natural language processing
• Extensive working knowledge of highly unconventional data sets
• Expertise in NLP, sentiment analysis, network/graph analysis, time series analysis, reinforcement learning, and computer vision
• Knowledge of constructing machine learning systems at the manufacturing level
• Strong time management, problem solving, and organizational skills