Quant Science

Building the future of quantitative finance education.

Co-Founder

Business Science

I help data scientists learn how to apply enterprise-grade machine learning in business & finance.

Identified gap in the market for data science courses that teach applied skills to solve real world business problems that led to multimillion dollar business

Scaled business to 3000+ students in first 3-years

Increased revenue 20% Year-over-Year

Used NLP and predictive lead scoring to segment email list leading to 30% subscriber growth

Sample Project: Pricing Recommendation Application (R, Shiny) 

And I teach all of these skills in my R-Track data science program: https://university.business-science.io/p/5-course-bundle-machine-learning-web-apps-time-series

Founder

Bonney Forge

Used predictive analytics to grow sales from $3M/Year-Over-Year to $15M/Year-Over-Year (500% increase)

Created a SQL database that stored leads

Trained technical sales staff to use the database for logging leads and requests for quotation

Applied predictive analytics to score leads & focus on highest likelihood of purchase (LOP)
Resulted in 5X increase in sales revenue

Director of Product Engineering & Market Development

Uncovered unmet demand through analysis of Technical Sales quotations leading to $6M Year-Over-Year business

Reviewed sales inquiry data on missed projects

Uncovered a pattern on projects with short lead times needing specialized products

Developed plans for Fast-Track Program & pitched program to CEO & VPs for buy-in
In first year, Fast Track generated $6M in revenue

Manager of Sales Engineering

Manages the Product Engineering Department, a team of engineers, designers, and document control personnel.
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HIGHLIGHTS:

► Led cross-functional project on key design change to improve fugitive emissions as a result of changing industry and EPA requirements. Design change affected core forged steel valve product line. Led team of senior managers in Operations, Purchasing, and Sales/Marketing. Transition occurred seamlessly with zero impact to customers. (ref. Valve Project Database and Analytics Implementation Project)

► Transitioned group from a support role within the organization to a project management-focused role. Increased efficiency of support functions through process improvement. Re-engineered processes, and developed standard operating procedures (SOPs).

► Aligned projects with strategic and organizational goals. Measured project success based on schedule and budget goals. Held individuals accountable for success of projects, which translated into exceptional performance.

► Oversees Document Control & CAD Management (Adept software) and Document Creation (product literature, videos, etc). Leveraged team's skills with SolidWorks and Adobe Creative Suite to develop media in-house.

Manager of Product Engineering

Bechtel Plant Machinery, Inc

Increased project management responsibilities within the Valves & Auxiliary Equipment department. Received two strategic business awards for leading a high-profile engineering project and response to a highly visible field issue.

Engineer II - Valves & Auxiliary Equipment

Project manager for engineering and design of manually-operated gate, globe, and check valves used within the primary propulsion system of Navy nuclear aircraft carriers and submarines. Primary responsibilities were to manage engineering projects and respond to field issues. Held a DOE L security clearance.

Engineer I - Valves & Auxiliary Equipment

I love data science. But, my story is a bit different. 

I began as an Excel-Guru / Mechanical Engineer. Not a traditional background for a data scientist by any means. 

I used Excel until I reached its limits. 

I found R, and the rest is history:

- I began learning R, which led to applying data science to business 

- By applying data science, my team went from $3M/year to $15,000,000 per year

- I began consulting, which led to trainings all over the world

- I found my passion for teaching, and made a company out of it (Business Science!)

Since finding data science, I haven't worked a day in my life. It's been amazing. 

Let me know if you'd like to learn R, Python, or just want to chat data science.

Matt Dancho

Anthony Alcaraz

Amazon Web Services (AWS)

Driving growth in the startup ecosystem by leveraging AWS's cutting-edge AI/ML technologies. My team helps startups accelerate their go-to-market strategies and technological development.

Key focus areas:
- Developing AI/ML startups
- Guiding high-potential startups through AWS's AI/ML offerings
- Fostering partnerships within the EMEA tech community
- Aligning AWS's AI/ML solutions with startup needs

Passionate about empowering the next wave of AI-driven innovation. 

Let's connect and explore how AWS can supercharge your startup's AI/ML journey!

Senior AI/ML Strategist Startups & VC EMEA Strategic Pursuits

Fribl

Advisor

GenAI x HR 
Designed AI strategy
Lead engineering team 

With recruiting topping the list of concerns for many CEOs and companies spending upwards of $4,000 on average per hire, the need for innovation is clear. 

All too often, the arduous recruiting process leaves both employers and applicants frustrated after 42 days of effort with uncertainty if the right match was made...

At Fribl , we are leading the charge to transform this status quo, starting with reinventing the screening process using our proprietary GenAI technology enhanced by symbolic AI. 

This powerful combination doesn't just accelerate hiring, but actually personalizes the experience to precisely match candidates with roles that align to the company's unique culture - a monumental undertaking previously thought impossible.

As innovations like video interviews, CV parsers and AI recommendations shake up recruitment, we remain laser focused on four seismic shifts: Diversity, Advanced Interviewing, Data Analytics and AI Adoption. 

Our mission? To disrupt outdated screening tactics by simplifying the complex, multidimensional analysis of applicant credentials at scale to surface the ideal talents that personify an organization's ethos.

Chief AI/ML Product Officer & Partner

University of Oxford

Guest lecturer to Ajit Joakar Course : Artificial Intelligence: Generative AI, Cloud and MLOps (online)
Subject : the intersection of LLMs and graphs

AI/ML Guest Lecturer

Aldecis

My client portfolio was L’Oreal Finance Transformation 
AI for BI 

Management in AI Projects and Engineering Teams:
In my managerial role, I oversee AI projects and lead teams of engineers in crafting cutting-edge solutions. My focus is on driving strategic initiatives, ensuring projects are aligned with organizational goals, and fostering a culture of innovation and excellence. I take a hands-on approach to problem-solving and mentorship, ensuring that both projects and people reach their fullest potential.

Development in LLM/ML Ops:
My development expertise lies in the specialized domains of Large Language Models Operations (LLM Ops) and Machine Learning Operations (ML Ops). I am responsible for streamlining machine learning workflows from development to deployment. My work enables organizations to not only adopt but excel in leveraging machine learning technologies, facilitating continuous learning and adaptability across the board.

Marketing and Strategic Partnerships:
In the realm of marketing, I focus on building and nurturing strategic partnerships. My efforts are geared towards creating synergies with like-minded organizations and industry leaders. By forming valuable partnerships, I amplify brand visibility, foster innovation, and open doors to new business opportunities. The result is a win-win situation for all stakeholders involved, enhancing both market reach and impact.

Chief AI/ML Product Officer

CNAF

AI for performance management and customer support

I served as a Senior Executive after a competitive exam (EN3S). 
In this multifaceted role, I spearheaded a range of initiatives across key sectors such as Finance, Fraud Detection, Quality Control, Accounting, and Data & Performance Management.

Signature Project in ML Ops:
Facing the pervasive challenge of managing recurring incoming flows, I took the lead in conceptualizing and executing an avant-garde Proof of Concept (POC). This POC employed Azure ML-based ML Ops solutions, setting a precedent by being the organization's inaugural foray into Machine Learning Operations. The result was a transformative improvement in operational efficiency and service delivery.

Influence on Pluriannual Strategy 2023-2027 :
My pioneering project had a profound impact on the organization's multi-year strategic plan, specifically in the following domains:

Predictive Analytics: My efforts catalyzed the adoption of predictive analytics, enabling the organization to preemptively identify and mitigate challenges in benefits delivery. This proactive methodology revolutionized our service approach.

Sentiment Analysis: The launch of the "Lab Parentalité" was a critical milestone. Through the use of sentiment analysis, we gained unprecedented insights into parent feedback. This initiative not only enriched our data but also helped us better comprehend and meet the needs of parents.

Chatbots & Virtual Assistants: I played a key role in overhauling our customer service paradigm. By integrating cutting-edge chatbots and virtual assistants, we were able to enhance customer engagement across multiple platforms, including phone support, online portals, and mobile apps.

Personalization Algorithms: To elevate the user experience, we incorporated algorithms designed to customize services and recommendations. This bespoke approach significantly increased user satisfaction by tailoring the experience to individual preferences and needs.

Writes on ML/LLM Ops 🎨 | Follow me (4,8 K+) 🔔

As AWS's Senior AI/ML Strategist for EMEA Startups, I specialize in helping early-stage AI companies accelerate their development and go-to-market strategies. My expertise spans:

Technical Domains:
- Large Language Models (LLM) and GenAI architectures
- Knowledge Graphs and Vector Spaces
- MLOps and production AI systems
- Multi-agent and agentic AI systems

Strategic Value-Add:
- Technical due diligence for  AI investments
- Architecture optimization for AWS AI/ML services
- GTM strategy development for AI startups
- Deep expertise in evaluating GenAI applications

Recent Achievements:
- Guest lecturer at Oxford on LLMs and graph-based systems
- Led AI strategy for multiple venture-backed startups
- Published thought leadership on agentic AI systems
- Built and scaled ML platforms for enterprise clients

I'm passionate about helping VCs and founders navigate the technical and strategic challenges of building transformative AI companies. Happy to provide technical evaluation, architectural guidance, and strategic insights to help portfolio companies succeed.

Chief AI Officer & Decision scientist

Management in AI Projects and Engineering Teams:
In my managerial role, I oversee AI projects and lead teams of engineers in crafting cutting-edge solutions. My focus is on driving strategic initiatives, ensuring projects are aligned with organizational goals, and fostering a culture of innovation and excellence. I take a hands-on approach to problem-solving and mentorship, ensuring that both projects and people reach their fullest potential.

Development in LLM/ML Ops:
My development expertise lies in the specialized domains of Large Language Models Operations (LLM Ops) and Machine Learning Operations (ML Ops). I am responsible for streamlining machine learning workflows from development to deployment. My work enables organizations to not only adopt but excel in leveraging machine learning technologies, facilitating continuous learning and adaptability across the board.

Marketing and Strategic Partnerships:
In the realm of marketing, I focus on building and nurturing strategic partnerships. My efforts are geared towards creating synergies with like-minded organizations and industry leaders. By forming valuable partnerships, I amplify brand visibility, foster innovation, and open doors to new business opportunities. The result is a win-win situation for all stakeholders involved, enhancing both market reach and impact.

Your AI agents can't reason about what they can't structurally represent. ⭕ 

The gap between demos and deployable agents comes down to one thing: knowledge representation strategy.

Here's when knowledge graphs become non-negotiable for agentic systems:

🔗 Multi-hop inference is core to your reasoning tasks

Vector embeddings excel at semantic similarity but collapse on relationship queries. When your agent needs to traverse "Service → DEPENDS_ON → Database → PROTECTED_BY → SecurityGroup" to assess blast radius, you need graph topology. Property graphs give you O(1) relationship traversal. Document stores force you to reconstruct connections at query time.

⚡ You're hitting the relational complexity threshold

The 30-table heuristic is real. Beyond this, JOIN complexity explodes exponentially. More critically: when understanding connections requires traversing 3+ relationship types simultaneously, relational schema optimization becomes the bottleneck. Graph databases are optimized for exactly this access pattern.

🧠 Your agents must handle temporal reasoning and contradictory perspectives

Event-centric schema patterns capture causality. Multi-perspective patterns represent conflicting data sources with confidence scores and provenance. Try modeling "Terraform says X, AWS API says Y, confidence scores differ" in a relational schema without creating a maintenance nightmare.

🔄 Entity resolution across fragmented systems is blocking coherence

When the same entity appears as "Bob Jones" in CRM, "Robert Smith Jr" in fraud detection, and "R.J. Smith" in compliance, your agent needs evidence-based resolution using relationship patterns and contextual validation. Graph neighborhoods provide the evidence structure for identity inference.

⚙️ The three-graph architecture matters for production

Separate domain graph (trusted structured data), lexical graph (unstructured source text), and subject graph (LLM extractions). Entity resolution creates CORRESPONDS_TO edges between them. This prevents extraction artifacts from polluting canonical knowledge while maintaining full provenance.

❌ Skip graphs when:

Simple retrieval suffices, no multi-hop reasoning required, or relationship density is low. RAG with vector search handles these cases at 1/10th the infrastructure complexity.

The technical reality: RDF gives you formal reasoning semantics but slower performance. Property graphs give you traversal speed but require explicit inference logic. Hypergraphs handle n-ary relationships elegantly but lack mature tooling.

Choose based on your reasoning requirements, not architectural trends.

What's your biggest challenge in getting LLMs to reason over complex, interconnected enterprise data?

Senior AI/ML Strategist Startups @AWS | O’Reilly Author | Business Angel

Most agentic systems hardcode their capabilities. 🔳

This does not scale.

Ontologies as executable metadata for the four core agent capabilities can solve this.

🔍 OBSERVE: Annotation-Driven Discovery

Instead of hardcoding entity recognition logic, mark identifying properties with inverseFunctional annotations in your ontology.

The agent queries at runtime: "What property uniquely identifies this entity type?"

Result: Generic retrieval code that works across any domain. Your agent constructs type-safe queries dynamically based on schema metadata.

🧮 REASON: Declarative Logic Execution

Extend SWRL with three operational primitives:

Negation as Failure - handle incomplete information and default rules
Existentials - create derived entities during reasoning chains
Aggregates - compute across collections (sum, count, max)

Store business rules as graph data. The reasoner executes them. Every inference is traceable back through the rule chain.

⚡ ACT: Three-Tool Generic Architecture

Build these once, use everywhere:

Tool 1: Semantic discovery (vector search + ontology enrichment)
Tool 2: Dynamic retrieval (queries ontology for addressing scheme)
Tool 3: Context expansion (queries ontology for traversal rules)

Mark relationships with contextualizing annotations. The agent discovers which paths to follow by querying your schema.

📈 LEARN: Self-Referential Improvement

Here's where it gets interesting. The system queries its own structure.
"What entity types lack dedicated tools?"
"Which relationships have high traversal costs?"
"What query patterns appear frequently but aren't optimized?"

Track tool usage, success rates, execution times. Feed this back into ontology refinement.

The agent proposes new tool definitions based on usage gaps. It optimizes reasoning paths through technical rules that don't change semantics.

🏗️ The Engineering Stack:

Co-locate ontology nodes with data in your graph database
Use annotations as queryable metadata (not documentation)
Implement dynamic query construction from ontology responses
Build generic tools that operate on ontological primitives
Enable runtime tool discovery through schema queries

Your ontology is live, queryable metadata that directly influences execution. Not documentation. 

⚙️ Implementation Pattern:

Agent receives task
↓
Query ontology for relevant entity types
↓
Query ontology for identifying properties
↓
Query ontology for contextualizing relationships
↓
Construct and execute dynamic query
↓
Return results with provenance

🎯 Why This Architecture Wins:

Domain experts can extend capabilities by modifying the ontology. No code changes. No redeployment. The agent adapts immediately.

Business logic lives in declarative rules that outlive implementation technologies. Your ontology is a lasting asset, not code that rots.

Systems can analyze their own schema to identify improvement opportunities.

Meta-programming becomes possible.

Your LLM based agent can't reason about what it can't traverse. ❌ 

After analyzing production agentic systems at scale, I've identified why most AI agents fail: they're built on vector embeddings and relational schemas that fragment knowledge into isolated records.

The agents that actually work? They're built on graph-based knowledge models that encode relationships as first-class primitives.

🏗️ Architecture pattern that's winning: The three-graph model

Separate your knowledge graphs by epistemic confidence:

Domain Graph: Canonical entities post-entity-resolution. Stable identifiers, validated relationships, single source of truth.

Subject Graph: LLM extractions with explicit uncertainty. Confidence scores, model versions, extraction metadata preserved.

Lexical Graph: Original unstructured sources. Complete provenance, immutable text chunks, enables RAG retrieval.

The killer feature? CORRESPONDS_TO relationships link Subject → Domain only after entity resolution validates the match. Your agent knows what's certain vs. extracted.

⚙️ The technical tradeoff matrix I'm using:

Property Graphs (Neptune, Neo4j): Fast traversals, intuitive semantics, but inference requires explicit coding

RDF + OWL: Native reasoning via formal logic, but 3-5x performance hit and complexity overhead

Hypergraphs: Natural n-ary relations (prescription linking doctor, patient, medication, dosage simultaneously), but immature tooling

Most production systems? Hybrid LPG for performance + RDF layer for reasoning tasks.

🎯 Implementation pattern: Selective vectorization + ontology-driven traversal

Don't vectorize everything. Curate your vector search space:

Index: Service names, error signatures, entity anchors

Exclude: Full configs, log streams, operational noise

Vector search handles ambiguous queries. Ontological annotations drive precise graph traversal for contextualization.

Result: Semantic flexibility + structural reliability.

📐 Entity resolution is your foundation

Evidence-based matching with Jaro-Winkler similarity + context validation. Not LLM guessing.

Explainable: "92% match because name=87%, address=100%, phone=95%"

This generates the stable node identities everything else depends on.

The companies shipping reliable agents aren't prompt engineering harder. They're building knowledge architectures that make invalid reasoning paths literally untraversable.

AI agents are economically broken at scale without caching ❌ 

A single user goal triggers 5-10 LLM calls in loops. At 10K daily conversations, you're burning $547K/year on a single workflow pattern.

The obvious fix? Semantic caching with vector embeddings.

The problem? It fails catastrophically for agents.

🔴 Why Semantic Caching Breaks:

Agents execute context-dependent reasoning chains. Each step builds on previous steps.

"Analyze Tesla's Q3 performance" vs "Analyze Tesla's stock price"

Cosine distance: 0.28 → Cache hit 

Actual equivalence: False

Your agent retrieves a financial analysis plan when the user wanted technical specs. 

The entire workflow executes the wrong reasoning path.

Tune your threshold tighter → 5% hit rate (still burning $520K) Tune it looser → 65% hit rate but 40% false positives

The F1 optimization doesn't solve context drift.

🔧 The Architecture That Works:

Hybrid Cache = Vector Speed + Graph Intelligence

Layer 1: Vector DB for fuzzy matching (~30% of redundant queries) 

Layer 2: Knowledge Graph storing reasoning structures, not text
Explicit entity disambiguation (Apple fruit ≠ Apple stock)
Multi-hop traversal for validating context alignment 

Layer 3: Validation gate before serving cached plans

🎯 The Critical Shift: Granularity

Don't cache full responses. Cache atomic sub-queries.
"Compare platforms" decomposes into:
FetchFeatures(Platform A)
FetchPricing(Platform A)
FetchFeatures(Platform B)
CompareAttributes(A, B)

These canonical sub-tasks repeat at 40-70% rates vs 5-15% for full workflows.

The graph stores execution plan templates. 
Vector search finds intent. 

Graph traversal validates the reasoning path applies to current context.

📊 Production Results:

Hit rate: 65% (vs 8% with semantic-only)
Precision: 95% (vs 40% with loose thresholds)
Cost: $547K → $191K annually (65% reduction)
Latency: 60% faster (cold → hot state)

Graph adds 20ms per query, but prevents false positives that would cost 2000ms+ in LLM calls or failed workflows

The Mental Model:

Traditional cache: "Have I seen this text?" 
Semantic cache: "Have I seen something similar?" 
Agentic cache: "Do I know how to solve this class of problem?"

You're not storing what agents said. 
You're storing how agents think.

The cache evolves from a lookup table to a reasoning substrate, the agent's long-term memory of validated strategies.

Agentic needs graphs. 

At scale, the hybrid architecture : the difference between agents that burn capital and agents that generate ROI.

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