Head of Data Science & Machine Learning

About Niche

Niche is the leader in school search. Our mission is to make researching and enrolling in schools easy, transparent, and free. With in-depth profiles on every school and college in America, 140 million reviews and ratings, and powerful search tools, we help millions of people find the right school for them. We also help thousands of schools recruit more best-fit students, by highlighting what makes them great and making it easier to visit and apply.

Niche is all about finding where you belong, and that mission inspires how we operate every day. We want Niche to be a place where people truly enjoy working and can thrive professionally.

 

About the Role

The data organization at Niche is seeking a dynamic and experienced Head of Data Science and Machine Learning to lead the organization's efforts in harnessing the power of data science, machine learning, and artificial intelligence. This is a high-impact role and the ideal person is expected to be a hands-on technical leader with strong data science expertise in the SaaS, B2C, or two-sided marketplace sector and will be accountable for establishing the data science and ML practices, capabilities and team ground up.  As the data science lead, you will be responsible for formulating and executing a comprehensive vision and roadmap for data science, ensuring the delivery of critical functionalities such as recommendation systems, personalized search, intelligent matching, generative AI, and predictive insights for customer health and retention.

At Niche we are revolutionizing the college admissions process using data and AI, making it more accessible and easier, and the person in this role will have an opportunity to accelerate that mission and make a tremendous impact on millions of students and families. This role will report to the VP of Data.

What You Will Do

During the 1st Month:

• Assessment and Onboarding: Conduct a thorough assessment of the current data science and ML infrastructure and capabilities.
• Challenges & Opportunities: Understand current challenges and identify immediate opportunities for leveraging data science and ML to enhance customer experience and business operations.
• Relationship Building: Develop relationships with key stakeholders to align on objectives and expectations.
• Quick Wins: Identify and implement quick wins to demonstrate the value and opportunities of data science and ML.

Within 3 Months:

• Vision and Strategy: Develop and communicate a clear vision and strategy for data science and ML within the company.
• Roadmap: Partner with stakeholders and data product manager to develop a comprehensive roadmap for data science and ML capabilities that reflects the strategic vision and business priorities,
• Development: Begin development work of the data science and ML roadmap and deliver quick wins
• Infrastructure: Work with the head of data engineering to identify infrastructure and platform needs that enable efficient development and productionization of data science and ML models
• Mentorship: Mentor and coach others within the larger data organization. 

Within 6 Months:

• Model Productionization:  Complete development and deploy initial models into production to optimize customer experience and customer retention.
• Model Optimization: Assess and refine the performance of these models based on data insights and stakeholder feedback.
• Product Integration: Collaborate with product and engineering teams to integrate AI-driven features and functionalities into our products and services.
• Org Development: Formalize the vision of the data science and ML organization along with resource needs based on the initial wins, learning, and strategic priorities that help unlock exponential and continuous impact. 

Within 12 Months:

• Start building the data science and ML team by recruiting and onboarding key talents fostering a culture of continuous learning, high performance, collaboration, and excellence.
• Achieve significant progress in delivering the planned roadmap. Quantify and demonstrate the impact on business outcomes of the data science and ML capabilities.
• Partner with senior leadership and cross-functional teams to identify and advocate potential opportunities for leveraging data science and AI, and influence strategic priorities and investments.
• Stay abreast of the latest developments and innovations in data science, ML, and AI through industry partnership and outreach and establish thought leadership thro