Jared Lander
Chief Data Scientist of Lander Analytics, Adjunct Professor at Columbia Business School & a Visiting Lecturer at Princeton University
Jared Lander
Chief Data Scientist of Lander Analytics, Adjunct Professor at Columbia Business School & a Visiting Lecturer at Princeton University
Biography
Jared P. Lander is Chief Data Scientist of Lander Analytics, the Organizer of the New York Open Statistical Programming Meetup and The New York and Government Data Science & AI Conferences, an Adjunct Professor at Columbia Business School, and a Visiting Lecturer at Princeton University.
With a masters from Columbia University in statistics and a bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. Jared oversees the long-term direction of the company and acts as Lead Data Scientist, researching the best strategy, models and algorithms for modern data needs.
This is in addition to his client-facing consulting and training. He is the author of R for Everyone (now in its second edition), a book about R Programming geared toward Data Scientists and Non-Statisticians alike. The material is drawn from the classes he teaches at Columbia and is incorporated into his corporate training.
Additionally, he is an R Consortium Board Member. Very active in the data community, Jared is a frequent speaker at conferences, universities and meetups around the world.
Speech Topics
Applied AI & Machine Learning
o Practical use of AI and LLMs in real-world systems
o AI agents and autonomous workflows for business insight
o Model selection, evaluation, and tradeoffs (accuracy vs. speed vs. interpretability)
o Building trustworthy, transparent AI systems
Geospatial Data Science & Location Intelligence
o High-performance geospatial analytics at scale
o Geospatial AI, anomaly detection, and forecasting
o Mapping and visualizing massive spatial datasets
o Turning location data into operational and strategic intelligence
Data Science in Secure & Regulated Environments
o Deploying analytics and AI on air-gapped or restricted networks
o Open-source data science in government and enterprise settings
o Balancing security, governance, and innovation
o Building compliant, reproducible data science infrastructure
Modern Data & Analytics Infrastructure
o Scalable data pipelines and workflow orchestration
o Databases for analytics (Postgres, DuckDB, columnar formats)
o Containerization and reproducible environments
o Performance optimization for large-scale computation
AI-Assisted Software Development
o Using LLMs and coding agents to accelerate development
o Prompt engineering for real engineering tasks
o Lessons learned from “vibe coding” and human–AI collaboration
o When AI helps, when it doesn’t, and how to use it effectively
Foundations & Best Practices in Data Science
o Understanding how algorithms actually work
o Choosing the right model for the problem
o Bias, variance, and overfitting explained intuitively
o From exploratory analysis to production systems
The Human Side of AI & the Future of Data
o Making sense of AI beyond the hype and understanding where it delivers real value
o The evolving role of data science in organizations and society
o Strategic frameworks for adopting AI in business and life
o Anticipating societal, ethical, and organizational implications of intelligent systems
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