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
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Biography
Jared P. Lander helps organizations turn complex data and artificial intelligence questions into practical strategies, reliable systems, and better decisions. He is the founder and Chief Data Scientist of Lander Analytics, a New York-based consulting and training firm that works with organizations to build data infrastructure, develop AI solutions, and move promising ideas into production. As Lead Data Scientist, Jared guides the firm’s long-term direction while remaining closely involved in client strategy, model development, technical implementation, and professional training. His experience spans a wide range of industries, including financial services, healthcare, government, professional sports, real estate, education, and technology.
Alongside his consulting work, Jared is an Adjunct Assistant Professor in the Decision, Risk, and Operations Division at Columbia Business School and a Visiting Lecturer at Princeton University’s Center for Statistics and Machine Learning. These academic roles keep him closely connected to the latest thinking in statistics, machine learning, and applied AI while giving him a strong understanding of how professionals learn technical concepts. He brings that same accessible teaching style to corporate workshops, helping teams strengthen their analytical capabilities and make more effective use of open-source tools.
Jared is also the organizer of the New York Open Statistical Programming Meetup and the New York Data Science & AI Conference and Government Data Science & AI Conference. Through these communities, he has spent more than a decade bringing together researchers, business leaders, government professionals, and technology practitioners to exchange ideas and examine how data science is being used in the real world. He has also served on the board of the R Consortium, supporting the continued growth of the R programming language and its global open-source community.
He is the author of R for Everyone: Advanced Analytics and Graphics, now in its second edition. Written for data scientists and professionals without formal statistical training, the book draws from the courses Jared teaches and has also shaped the hands-on instruction he provides to corporate clients.
As a speaker, Jared makes data science and AI understandable without oversimplifying the challenges involved. He combines technical expertise with practical examples to help audiences understand what modern AI can do, where projects often go wrong, and what organizations need to build before these technologies can deliver meaningful results. Jared holds a master’s degree in statistics from Columbia University and a bachelor’s degree in mathematics from Muhlenberg College.
Speaker Videos
How I Learned to Stop Worrying and Love Vibe Coding
Speech Topics
Applied AI & Machine Learning
AI creates value only when it moves beyond experimentation and begins solving real problems. Jared Lander shows organizations how to apply artificial intelligence, machine learning, and large language models within practical, production-ready systems. Jared explores the growing role of AI agents and autonomous workflows while examining where they can improve analysis, decision-making, and operational efficiency. He also explains how to select and evaluate models based on an organization’s priorities, including the tradeoffs between accuracy, speed, cost, and interpretability. This practical keynote helps audiences move from AI ambition to responsible implementation while building systems that people can understand and trust.
Audiences will learn:
How to identify practical applications for AI and machine learning
What to consider when selecting and evaluating models
How to build more transparent and trustworthy AI systems
Geospatial Data Science & Location Intelligence
Location data can reveal patterns that remain invisible in traditional analysis. Jared Lander explores how organizations can use geospatial data science to understand movement, detect anomalies, forecast change, and make better operational decisions. Jared explains how high-performance analytics and geospatial AI can transform massive spatial datasets into clear maps, useful models, and actionable intelligence. He also examines the technical and strategic considerations involved in working with location data at scale, from data quality and visualization to computational performance. This practical keynote shows audiences how to move beyond simply mapping information and use geography as a powerful tool for planning and decision-making.
Audiences will learn:
How geospatial analytics can uncover meaningful patterns and risks
Ways to apply AI, anomaly detection, and forecasting to location data
How to turn complex spatial datasets into operational and strategic insight
Data Science in Secure & Regulated Environments
Innovation becomes more complicated when data cannot leave the network and every system must meet strict security requirements. Jared Lander explores how organizations can build and deploy analytics, machine learning, and AI within air-gapped, restricted, and highly regulated environments. Jared examines how open-source tools can support government and enterprise teams while maintaining control over data, infrastructure, and governance. He also explains how reproducible workflows and well-designed systems can help organizations meet compliance standards without slowing meaningful progress. This practical keynote gives audiences a clearer path toward building data science capabilities that are secure, dependable, and ready to scale.
Audiences will learn:
How to deploy analytics and AI within restricted environments
Ways to balance security and governance with continued innovation
How to build compliant, reproducible data science infrastructure
Modern Data & Analytics Infrastructure
Powerful analytics depend on infrastructure that can keep pace with growing data and increasingly complex workloads. Jared Lander explores how organizations can design scalable pipelines, orchestrate workflows, and select the right database technologies for modern analytical needs. Jared examines tools such as Postgres, DuckDB, and columnar data formats while explaining how containerization and reproducible environments can make systems more reliable and easier to maintain. He also addresses the performance challenges that emerge at scale and the practical choices teams can make to improve speed without adding unnecessary complexity. This technical yet accessible keynote gives audiences a clearer framework for building data infrastructure that supports efficient analysis, collaboration, and long-term growth.
Audiences will learn:
How to design scalable data pipelines and analytical workflows
What to consider when selecting databases and data formats
How reproducible environments and performance optimization strengthen analytics infrastructure
AI-Assisted Software Development
AI can accelerate software development, but only when teams understand where it adds value and where human judgment still matters. Jared Lander explores how large language models and coding agents are changing the way developers write, test, and improve software. Jared shares practical approaches to prompting AI for real engineering tasks while examining the lessons emerging from “vibe coding” and other forms of human-AI collaboration. He also addresses the limitations of these tools, including unreliable output, hidden errors, and the risk of moving faster without understanding the code being produced. This practical keynote helps technical teams use AI more effectively while maintaining quality, accountability, and sound engineering judgment.
Audiences will learn:
How LLMs and coding agents can support software development
When AI-generated code should be trusted, tested, or rejected
How to combine AI assistance with strong engineering practices
Foundations & Best Practices in Data Science
Strong data science begins with understanding the problem, not choosing the most complicated algorithm. Jared Lander makes the foundations of data science accessible by explaining how models work, why they succeed or fail, and how to select the right approach for a particular challenge. Jared offers an intuitive look at concepts such as bias, variance, and overfitting, helping audiences understand the tradeoffs behind model performance without getting lost in unnecessary complexity. He also walks through the full data science lifecycle, from exploratory analysis and experimentation to reliable production systems. This practical keynote gives technical teams and organizational leaders a stronger foundation for evaluating models, interpreting results, and building solutions that work beyond the lab.
Audiences will learn:
How to match the right model to the problem
Why bias, variance, and overfitting affect performance
What it takes to move from exploration to production
The Human Side of AI & the Future of Data
The most important questions about artificial intelligence are not only technical. They are about how people will work, make decisions, and adapt as intelligent systems become more capable. Jared Lander helps audiences look beyond the hype to understand where AI is delivering real value and how the role of data science is evolving within organizations and society. Jared offers practical frameworks for deciding when to adopt AI, where human expertise remains essential, and how leaders can prepare for changes that extend beyond technology itself. He also explores the broader ethical and organizational implications of intelligent systems, helping audiences approach the future with informed judgment rather than fear or unrealistic expectations. This thought-provoking keynote brings the human consequences of AI into focus while giving audiences a clearer path for navigating what comes next.
Audiences will learn:
How to distinguish valuable AI applications from exaggerated claims
How the role of data science is changing within organizations and society
What leaders should consider when evaluating AI’s human and organizational impact
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