Principal
Ross Katz
Principal & Data Science Lead
Ross sits at the nexus of strategy and data science, helping client leadership teams turn data into operating leverage rather than slide decks. Most of his work is in biotech and life sciences, e-commerce, and SaaS — sectors where the difference between a useful model and a shelved one comes down to translation between the data team and the people running the business.
Connect on LinkedInClient work
Embedded Analytics Modernization for a Payroll SaaS Platform
CorrDyn built an embedded analytics PoC for a payroll platform, achieving sub-second latency and 6-minute data freshness at 75% lower cost.
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Government Reporting Overhaul for a Home Care Platform
CorrDyn stabilized 100 mission-critical CMS reports for a home care company ahead of a hard February deadline, then transitioned to ongoing data team support.
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ML Customer Segmentation for an Automotive Dealer Group
CorrDyn built an ML clustering pipeline on Databricks with LLM-powered profiling to segment customers across vehicle brands for a dealer group.
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Embedded Analytics for a Healthcare Data Platform
CorrDyn built HIPAA-compliant embedded dashboards for a healthcare data company, going from assessment to production in a 45-day proof of concept.
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Data Pipeline and BI for an Investment Networking Platform
CorrDyn built reverse ETL pipelines and a Looker BI platform for an alternative investment networking company, unifying CRM, financial, and application data.
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Secure Data Pipeline for a Global Research Organization
CorrDyn built encrypted data extraction from five survey platforms into MotherDuck with PII separation for a research nonprofit.
Read the full storyPodcast episodes with Ross
Browse all podcasts
Why Nvidia's $13B Hugging Face Deal Was Never About Revenue
with Ross Katz & Jason Bradwell
DrugBank CEO: Why Your AI Model Gives You Half the Answer
with Lisa Downey, DrugBank
The $1 Million Threshold for Self-Hosting Open-Weight AI Models
with Ross Katz & Jason Bradwell
Genomic Data Bias, Fuzzy Phenotypes, and Drug Target Discovery
with Erika Kvikstad, Independent
How to Build a Data Team Ready for AI Agents
with Ross Katz & Jason Bradwell
From Single-Cell Data to Cell-Depleting Therapies
with Adam Freund, Arda Therapeutics
What the Prefect-Dagster Merger Means for Your Orchestration Stack
with David Yaffe, Estuary
Why Biotech Talks About AI But Won't Pay for the Data It Needs
with John Androsavich, Ginkgo Datapoints
How Welocalize Made an AI Agent Earn Its Way Into Production
with Matthew Sekac, Welocalize
Physical AI for Drug Discovery: Beyond Language Models
with Woody Sherman, PsiThera
The Consulting-Lab Land Grab: What Sits Above the Model
with Ross Katz & Jason Bradwell
Similar Keynote, Different Platforms: Snowflake vs. Databricks
with Ross Katz & Jason Bradwell
Articles by Ross
Don’t Outsource Your Agentic Analytics Stack, Compose It
Out-of-the-box agentic analytics systems lock your semantic layer, context, evaluation, and feedback loop inside one product. Those four belong to you.
MotherDuck Guides: What to Put in Them, and What to Push Down
MotherDuck Guides are a routing layer for analytics agents, not documentation. What to put in them, what to push down, and how to keep them correct.
The Context Layer: What to Build, What to Skip, and Where to Start
A staged framework for deciding which data architecture components AI agents need, when to build them, and why, based on the value you are creating.
A Map of Where AI Creates Value in Your Business
Use case lists are not strategy. A mechanism-based framework for identifying where AI creates value, organized by what the AI is doing.
The Maintenance Trap: Why AI-Accelerated Data Teams Feel Slower
AI accelerates the 21% of data work that is code generation. The other 79% is maintenance, and every new asset makes it worse.
MCP in the Enterprise: Cost, Security, and Workload Routing
MCP is now core infrastructure. Its real cost at enterprise scale, where the security model breaks, and how to route agent workloads deliberately.
Skip the Data Stack, Get the Wrong Answer Faster
AI agents querying raw source systems inherit every data quality problem the transformation layer solves — then present wrong answers with confidence.
SaaSpocalypse: Who Deserves the $300B Software Selloff
An 8-question diagnostic framework for assessing which SaaS businesses AI threatens, validated against 2026 YTD stock performance.
LLM Tools for Data Apps: Architectural Components
How to decompose LLM workflows into task components, break down complex RAG systems, and select tools using 10 evaluation principles.
7 Principles: Which LLM Business Cases Will Work and Which Will Fail
A practical framework for evaluating LLM use cases in enterprise settings, separating hype from high-ROI opportunities.
11 Questions: Open LLM vs Third-Party LLM API
A framework of 11 critical questions to evaluate whether your use case needs an open-source LLM or a third-party API like OpenAI or Claude.
How Biotechs Can Squeeze Value from Lazy Data
Why biotech data sits underutilized, why off-the-shelf solutions fall short, and how an external data team delivers quick wins under $150K.
6 Ways Biotech Manufacturers Can Unlock Data Value
A practical guide for biotech CMOs to unlock value from manufacturing data through process optimization, compliance, digital twins, and predictive maintenance.
5 Ways to Screw Up Your Digital Transformation
Five common mistakes that derail digital transformation projects and how to avoid them, from data management to change management.
How to Start Event-Based Marketing: A Bottom Up Approach
A bottom-up approach to event-based marketing using data integration, customer behavior analysis, and automation to optimize marketing spend.
10 Reasons to Hire an External Data Team
Ten reasons to partner with an external data team for your data projects, and four situations where building an internal team makes more sense.
117 Key Questions for E-Commerce Leaders
117 questions e-commerce leaders should answer across finance, marketing, sales, operations, support, technology, and BI — prioritized by business impact.
How to Budget for Data Science and Data Engineering Work
A framework for budgeting data science and engineering projects using impact vs. complexity scoring, with surveys for assessing both.
How to Understand Your Customer Acquisition Cost and Lifetime Value
A practical guide to calculating Customer Acquisition Cost and Lifetime Value, and using the CAC:LTV ratio to manage growth and profitability.
3 Drivers of Data-Savvy Organizations
How data availability, actionability, and credibility determine whether your organization captures value from its data investments.
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