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Harvard Business School · 3 days ago
Senior Data Scientist - Generative AI
United States
Full-time
Remote
Senior Level
7+ years exp
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Hiring Manager
Kavitha Ratnam
Insider Connection @Harvard Business School
Responsibilities
Architect the overall framework and infrastructure for GenAI products like search interfaces, bots, summarizers, etc. Develop and implement techniques to optimize model performance to meet specific product goals
Collaborate closely with product management and engineering leads to align on technical roadmap. Guide engineering teams to effectively leverage LLM capabilities in product implementations
Establish protocols and systems for building fair, accountable and transparent LLM-based applications. Lead efforts to proactively assess and mitigate risks due to model biases or failures
Implement robust feedback pipelines, monitoring and corrections to ensure model safety
Design and oversee curation of high-quality datasets tailored for LLM training for each product. 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.
Communicate clearly and effectively to technical and non-technical audiences, verbally and visually, to create understanding, engagement, and buy-in. Contribute novel research and analyses to leading academic conferences and journals.
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. Proactively analyze latest developments in large language models to deeply understand model capabilities, limitations, and best practices. Develop techniques to continually improve language understanding and model training
Embody the values and passions that characterize Harvard Business School, with empathy to engage with colleagues from a wide range of backgrounds.
Mentor and develop junior data scientists in state-of-the-art GenAI methods
Set technical vision and lead initiatives to accelerate product impact through cutting-edge LLM innovations
Complete other responsibilities as assigned.
Qualification
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Required
Minimum of seven years’ post-secondary education or relevant work experience
Minimum of three years’ experience in developing machine learning models with a track record of creating meaningful business impact and working with multiple stakeholders
Experience with production RAG pipelines and agentic information retrieval and search systems, with the ability to write production level code
Minimum of five years’ experience with Python
Minimum of three years' experience building production NLP and deep learning models using PyTorch/Tensorflow, along with using large language model architectures (BERT, GPT-3 etc.)
Experience building advanced workflows such as retrieval augmented generation, model chaining, dynamic prompting, PEFT/SFT, etc. using Langchain and similar tools
Experience establishing model guardrails and developing bias detection and mitigation techniques for AI applications
Proficiency with various prompting techniques, with a clear understanding of tradeoffs between prompting and fine tuning
Experience with fine tuning embedding models and tuning vector databases to improve performance of semantic search and retrieval systems
Deep understanding of underlying fundamentals such as Transformers, Self-Attention mechanisms that form the theoretical foundation of LLMs
Experience with cloud computing platforms and tools (AWS, GCP, or other)
Experience operationalizing end-to-end machine learning applications
Demonstrated team performance skills, service mindset approach, and the ability to act as a trusted advisor
Preferred
Advanced degree in mathematics, physics, computer science, engineering, statistics, or an equivalent technical discipline desired
Completion of Harvard IT Academy specified foundational courses (or external equivalent) preferred
Benefits
Paid Time Off: 3-4 weeks of accrued vacation time per year, 12 accrued sick days per year, 12.5 holidays plus a Winter Recess in December/January, 3 personal days per year, and up to 12 weeks of paid leave for new parents who are primary caregivers.
Health and Welfare: Comprehensive medical, dental, and vision benefits, disability and life insurance programs, along with voluntary benefits.
Work/Life and Wellness: Child and elder/adult care resources, Employee Assistance Program, and wellness programs related to stress management, nutrition, meditation, and more.
Retirement: University-funded retirement plan with contributions from 5% to 15% of eligible compensation, based on age and earnings with full vesting after 3 years of service.
Tuition Assistance Program: Competitive program including $40 per class at the Harvard Extension School and reduced tuition through other participating Harvard graduate schools.
Tuition Reimbursement: Program that provides 75% to 90% reimbursement up to $5,250 per calendar year for eligible courses taken at other accredited institutions.
Professional Development: Programs and classes at little or no cost, including through the Harvard Center for Workplace Development and LinkedIn Learning.
Commuting and Transportation: Various commuter options handled through the Parking Office, including discounted parking, half-priced public transportation passes and pre-tax transit passes, biking benefits, and more.
Harvard Facilities Access, Discounts and Perks: Access to Harvard athletic and fitness facilities, libraries, campus events, credit union, and more, as well as discounts to various types of services (legal, financial, etc.) and cultural and leisure activities throughout metro-Boston.
Company
Harvard Business School
Harvard Business School is an educational institution that offers two-year residential programs leading to an MBA, Ph.D., or DBA degree. It is a sub-organization of Harvard University.
1,001-5,000 employees
Funding
Current Stage
Late StageLeadership Team
Doug Levin
Guest Lecturer, Harvard Business Analytics Program (HBAP)
Katherine Manweiler
MBA Candidate
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