Position Description

Be a pioneer in business, education, and global impact by joining the Harvard Business School Digital Transformation team - a "startup with assets," where you will have the chance to deploy cutting-edge digital- and emerging-technology education solutions. Where else can you make a difference at the intersection of cutting-edge technology, world-class education, noble purpose, and timeless legacy?

As a Senior Data Scientist you will collaborate with the Data Science and Machine Learning team and will create data science, machine learning, and AI solutions to better address the needs of our constituents (students, alumni, faculty, researchers, staff, and community at large). You will have the chance to guide and continuously improve the ways in which we engage, educate, and empower people around the world, combining the best of human touch and technology scale. You will experiment with everything from the latest AI algorithms and techniques to blended and immersive environments, multi-modal and varied-form content, and the most innovative research and teaching methodologies.

In this capacity, you will translate the needs of our cross-functional stakeholders into predictive models and production-grade algorithms and platforms and will drive value creation through personalized engagement, expanded reach, and experimental new ways of learning that will continue the Harvard Business School leadership in education, business, and societal impact.

Responsibilities Include:

• Develop analytical models and solutions / production-ready algorithms that solve real business problems, taking into account business needs and technology/operations landscape; lead interaction with internal stakeholders and technology on specific projects and initiatives.
• Apply data science, machine learning, and AI techniques to derive business value from the full range of internal and external data sets in a cloud environment.
• Build data science pipelines from feature generation, data visualization and models evaluation; design the solution, build initial code and provide documentation with ways of working to maximize time to value and re-usability.
• Translate complex data and methodology into strategic, operationally feasible insights and recommendations; automate implementation.
• Communicate clearly and effectively to technical and non-technical audiences, verbally and visually, to create understanding, engagement, and buy-in.
• Identify trends and opportunities to drive innovation, both in what we do and how we do it; evaluate new data science, machine learning, and AI technologies and tools that can boost team performance, innovation and business value.
• Embody the values and passions that characterize Harvard Business School, with empathy to engage with colleagues from a wide range of backgrounds.
• Promote data science, machine learning, AI, and digital and emerging technologies at Harvard Business School in relevant channels through community engagement, networking, speeches, and publications as applicable.
• Responsible for other duties as assigned

Basic Qualifications

• Minimum of seven years' post-secondary education or relevant work experience

Additional Qualifications and Skills

Other Required Qualifications:

• Advanced degree in mathematics, physics, computer science, engineering, statistics, or an equivalent technical discipline.
• Minimum of three years' experience in developing machine learning models with a track record of creating meaningful business impact and working with multiple stakeholders.
• Minimum of five years' experience with Python and SQL.
• Experience with cloud computing platforms and tools (AWS, Google Cloud Platform, or other).
• Expertise in multivariate statistical modelling (e.g. clustering, regression, principal components and factor analysis, time-series forecasting, Bayesian methods) and machine learning (Random Forest, KNN, SVM, boosting and bagging, regularization etc.)
• Proficiency with data visualization tools (D3.js, R Shiny, Looker, Streamlit, or similar).
• Experience operationalizing end-to-end machine learning applications.

• Experience with neural networks, deep learning, and reinforcement learning, using frameworks such as TensorFlow.
• Experience with Natural Language Processing (NLP), Large Language Models (LLMs), and/or Recommendation Engines.