Serg Masís

I'm a Data Scientist in agriculture with a background in entrepreneurship and web/app development and the author of the book "Interpretable Machine Learning with Python" and the upcoming book "DIY AI". I'm passionate about data-driven decision-making, Responsible AI, behavioral economics, and making AI more accessible.

I'm pleased that my recently published second edition has a 4.9 in Amazon, as well as garnered the following accolades: #1 Most Wished For, #2 Best Seller, #1 New Release and Best A.I. Books of All Time by Book Authority. To learn more about my book click here.

What I Do

Analytics & Visualization

I wield statistical tools and methods to derive insights from data. As a web designer in a previous life, I'm a visual communicator by nature. I find the best ways to let the data do the storytelling.

Deployment & Evaluation

As a former webmaster, I put a lot of care into deployment procedures and monitoring performance. For machine learning models, it is critical to adhere to strict procedures and constantly monitor model performance.

Predictive Modelling & Interpretation

I am comfortable with numerous machine learning techniques, including: regression, classification, clustering, and dimensionality reduction problems; and model interpretation and causal inference.

Management, Writing & Speaking

I have managed projects and teams since 2006. This includes defining scopes, executing plans, technical writing, troubleshooting endlessly, mentoring, and engaging with stakeholders. It also includes speaking in board rooms and, more recently, speaking at conferences.



20 years experience


Packt Publishing


Wrote the bestselling book titled Interpretable Machine Learning with Python for Packt, a UK based publisher. Now, working on the second edition as well as co-authoring one called "Responsible AI".


Climate & Agronomic Data Scientist

Syngenta is a leading agriculture company helping to improve global food security by enabling millions of farmers to make better use of available resources.


Data Scientist | Systems Analyst Fellow

Formlabs is a 3D printing technology developer and manufacturer specialized in making desktop stereolithography printers.

• Examined data warehouse for data properties and quality issues as well as missed opportunities.

• Surveyed business stakeholders to deconstruct common business reporting, metrics, and key performance indicators.

• Wrote a “State of Data” report and executive-level presentation which highlights best-performing areas as well as concrete recommendations and prioritized strategy to improve existing processes and metrics. Including best strategy for leveraging behavior data to customer clustering.

• Predicted 3D printer failure with cascaded gradient boosted decision trees for my summer data science practicum.

• Used customer behavior data to improve understanding of life time value for financial planning and analysis office.

• Found discrepancies between different data sources for sales data and reconciled them with a new engineered view.

2015 - 2017

Head of Product Development | Co-Founder

Shuflix is a search engine that combines the power of cloud computing with principles in decision-making science to efficiently expose users to new places and events around them.

• Built entire engine using big data cloud infrastructure and machine learning techniques to assist decision making that managed to become the 2nd largest search engine for places and events offering 4.5 million items/day

• Incubated for 2 terms at Harvard Innovation Lab, and 1 term at Yale Entrepreneurial Institute where acquired many business, startup, project management and design thinking skills

• Finalist at Harvard President's Innovation Challenge 2017, Harvard's most prestigious business competition

2013 - 2016
Winning Poker Network


WPN is a 17-year old company that manages many brands in the online gaming industries including Americas Card Room, now the 4th largest online poker room in the world.

• Installed and monitored a statistics engine to display sales funnel and other data to inform new marketing plans, HR policies, development and business strategies which helped grow company during my tenure.

• Developed automated reporting system that gathered the team’s work progress metrics and website statistics to alert stakeholders about project statuses and priorities reducing up to 2 hours/day in manual work.

• Designed and implemented new software deployment; as a result, testing and monitoring procedures were reduced from hours to minutes despite the introduction of new tiers of approval from management.

• Led a team to expedite all website project requests and correct bug fixes with custom-built tools.

2011 - 2013
SafeT, Inc

Mobile App Developer | Client-Side Architect

SafeT was a personal command center that assists users in any type of emergency through real-time coordination with large datasets of current events, social networks and medical networks.

• Programmed the client side for both Android & iOS.

• Trained and mentored a junior programmer.


Product & Brand Developer | Founder

T++ was the first bubble tea shop in Costa Rica, focused on local innovative flavors, art and community outreach.

• Introduced product successfully to the country where 2 locations opened within first year and was featured on national news media frequently.

• Leveraged customer behavior with sales analytics and loyalty program and social media analytics to increase monthly sales and brand awareness.

• Created a hub for emerging artists to showcase and sell their fine art. Several are now well known in the country.

Global Gaming Labs

Project Manager | Online Marketing Consultant

• Devised production of audiovisual and web marketing materials to improve sales and promote the products.

• Performed digital marketing campaigns management and analytics for partners and clients.


Director of Web Development

SIDI was a large call center that operating a dozen sports betting brands including Justbet and Guardian Guarantee.

• Integrated and maintained a fully automated Customer Relationship Management system that was used by all sales and customer service agents that featured sophisticated workflows and VoIP integration with custom-built modules.

• Performed ETL regularly on purchased datasets for mass mailing and other marketing initiatives.

• Analyzed web stats in relation to CRM metrics and financials on a regular basis to derive business insights.

• Led a team to transition in record time from one costly and ineffective software platform, whose license was about to expire, to another platform allowing the company to save millions of dollars and to continue operations.

Outcoding Inc.

C# Software Developer

Outcoding is a nearshore outsourcing company.

• Developed in-house reporting, customer service and game importing systems for a back-office system in use for several sportsbooks

• Designed and implemented an interface module that tied different components designed by other team members.

2002, Winter
Artech Digital Entertainment

3D Modeling, Intern

Artech Studios is a video game developer based out of Ottawa, Canada.

• Modeled three dimensional characters and props for an in-house PC / PlayStation / X-box game in development called Raze's Hell (working title: Schnozz).


Web Developer | Project Manager

The, now-defunct, began operations in October, 2001. It was an events collaboration site. Members would use built-in email, chat, forum and event listing functionality to share and comment about electronic music events (raves) worldwide. It was innovative because was built entirely in Flash – which was a huge challenge back then – and was one of the first user-generated sites in the event space and a social media site built before Myspace and Facebook!

• Developed the web-mail interface and events collaboration modules from scratch

• Integrated pre-existing forum and chat components into something more interactive complete with member profiles and feeds.

• Designed and Implemented a large scale web-scraping operation to gather content from events from other sites using automated cronjobs and ETL.

Although the startup got hundreds of thousands of signups, it failed to monetize in time and had to shut down. However, due to the innovative nature of this project, we were invited to speak in a small conference in Prague in August, 2003.


Illinois Institute of Technology

Master’s in Data Science

Relevant coursework: Deep Learning, Statistical Learning, Computer Vision, Big Data Technologies, Applied Statistics, Data Preparation and Analysis, Machine Learning in Finance

Latin American University for Science And Technology

BS in Computer Science Engineering

Relevant Coursework: Probability and Statistics, Discrete Mathematics, Calculus for Engineering, Data Structures, Numerical Methods, Quality and Risk Assesment, Analysis and Design of Systems I & II, Databases, Programming I-VI, Financial Engineering, Operations Research I & II, Industrial Accounting, Teleinformatics I & II



• Native English and Spanish

• Intermediate proficiency in French

Additional Skills

• 3D modeler/animator

• Graphic designer


• Gourmet cook with a love for coffee & tea beverages.

• Movie and music knowledge guru.

Data Wrangling & Statistical Analysis

Python (Pandas, Numpy, SciPy, StatsModel, NLTK, Gensim,...)


R (Tidyverse, Caret, purrr...)


SQL (most variations)


Excel (Pivot tables, vlookup, VBA)


Tableau Prep


Machine Learning





Interpretability (SHAP, LIME,...)


R (randomForest, lme4, glmnet,...)




XGBoost, Catboost, LightGBM


Tuning & Experimentation (Hyperopt, Weights & Biases..)


Distributed Training (Elephas, Horovod)


Computer Vision







Data Visualization

Python (Matplotlib, Seaborn)


R (Ggplot, Plotly, Leaflet)


Tableau Desktop




Big Data & Distributed Systems





Apache Spark










Web & Mobile Development









HTML + CSS + Javascript








Objective C




Computer Graphics

Adobe Photoshop


Adobe Illustrator


Adobe After Effects


Autodesk 3d Studio Max


Rhino 3D


Project Management

Agile Methodologies


Team Leadership


Public Speaking


Git (also Github/Bitbucket)






Operating Systems & Server Config

MacOS (inc. Terminal)


Ubuntu / CentOS (inc. Command Line)




Shell Scripts (and Crontab)


Python Virtualenv





3 Exciting Books!

I am deeply committed to advancing the field of artificial intelligence (AI) with a focus on making it more interpretable and responsible. In my writing, I address the critical importance of fairness, transparency, and accountability in AI technologies and I champion the idea that AI models should not only be advanced in their capabilities but also understandable and ethical in their applications. I have authored a bestselling book on interpretable machine learning, illustrating my dedication to enhancing how models can be deciphered and scrutinized for better decision-making processes. Furthermore, my collaborative efforts on responsible AI highlight the growing necessity to address ethical considerations and societal impacts as AI becomes increasingly integrated into various aspects of life. My work transcends specific domains, aiming to foster a broader understanding and engagement with AI technologies. I believe in empowering individuals and communities with the knowledge and tools to participate actively in shaping the future of AI, ensuring it serves the common good and addresses global challenges with responsibility and care, which is why I'm working on the DIY AI book.

Interpretable Machine Learning with Python—Oct. 2023


  • A comprehensive introduction covers white-box models like linear regression and decision trees and the fundamentals of interpretability and explainability.
  • Look under the hood of black-box models with model-agnostic methods such as SHAP, anchors, and counterfactuals, which can make complex machine-learning models understandable and accountable. It covers methods for understanding deep learning models for vision and text and features advanced techniques for causal inference and uncertainty.
  • Become a machine learning model mechanic by leveraging techniques like bias mitigation, feature selection, and adversarial robustness
  • The book's intended audience is made up of data scientists, machine learning engineers, MLOps engineers, and those interested in responsible AI development. It is suitable for beginners with a solid foundation in Python and can act as a bridge to understanding the relationship between AI and the real world, promoting ethical technology development.

For more details about the book and links to where to buy, click here.

DIY AI: Step-By-Step Artificial Intelligence Projects for Makers and Hackers—Sep. 2024!

🔥 pre-order now🔥

  • Explore the essentials of AI, machine learning, and deep learning, including their definitions, histories, types, and applications, while also setting up your Python environment for AI projects.
  • Dive into Discriminative AI with interactive AI projects for facial recognition, sound classification, pose estimation, gesture recognition, and action recognition for dynamic applications and sentiment analysis to monitor social media mood.
  • Create with Generative AI art, music, and chatbots, and responsibly develop deepfakes with step-by-step guides on projects that can be integrated into web apps.
  • The book's intended audience is very broad, from aspiring and established AI practitioners seeking hands-on experience to citizen developers, hobbyists, and DIY enthusiasts eager to experiment with AI through open-source technologies catering to diverse interests and skill levels.

For pre-ordering click here.

Building Responsible AI with Python: Learn to identify and mitigate bias with hands-on code examples—May 2025!

  • Understand the principles of Responsible AI, including auditing models for group and individual fairness, and apply these concepts using hands-on Python techniques to ensure AI-enabled solutions are safe and fair.
  • Explore various explanatory techniques to gain insights into the logic of complex machine learning models, enhancing transparency and trust in AI applications.
  • Implement pre-processing, in-processing, and post-processing techniques to mitigate bias in the development of machine learning models, ensuring equitable outcomes.
  • Monitor machine learning models in production environments to identify and manage model drift, thereby maintaining accuracy and fairness over time.
  • This book's intended audience is data scientists, machine learning developers, and data science professionals seeking to create non-biased, accurate machine learning models. A working knowledge of Python and basic machine learning concepts is recommended.

For more details about the book, click here.




due to paternity

Past Events

Oct. 31 - Nov. 2, 2023

ODSC West 2022 (San Francisco, CA) 🇺🇸


"Facial Recognition from Scratch with Python and JS" (workshop)

Oct. 24, 2023

MLOps World + Generative AI World Summit 2023 (Austin, TX) 🇺🇸

MLOps World

"Quality Assurance (QA) in Machine Learning" (talk)

June 19-22, 2023

ML Conference Munich 2023 🇩🇪

ML Conference

"Interpreting NLP Transformers" (session)
"Introduction to Explainable AI" (workshop)

May 24-25, 2023

Infoshare 2023 (Gdańsk) 🇵🇱


"DIY AI: Facial Recognition from Scratch" (session)
"QA for AI" (talk)

May 11-12, 2023

Data Innovation Summit 2023 (Stockholm) 🇸🇪

Data Innovation Summit

"QA for AI systems" (talk)

Apr 27, 2023

DeveloperWeek Europe 2023 🇪🇺


"QA for AI Systems" (keynote)

May 9, 2023

KMD Steam 2023 (Copenhagen) 🇩🇰


"Come play with AI! The Importance of Play in Machine Learning" (talk)

April 26, 2023

Semantic Layer Summit 2023 🇺🇸

Semantic Layer

"Data & Analytics Governance Strategies for Driving Growth & Preventing Anarchy" (panel)

March 7, 2023

Convergence 2023 🇺🇸

ML Convergence

"QA for AI Systems" (talk)

Dec. 1, 2022

ML Berlin Hybrid Conference 2022 🇩🇪

ML Conference

"Making Machine Learning Models Attack-Proof with Adversarial Robustness" (workshop)

Nov. 11, 2022

Build Stuff 2022 (Vilnius, Lithuania) 🇱🇹

Build Stuff

"Adventures in Puppy Training with A.I. and a Raspberry Pi" (talk)

Nov. 2, 2022

ODSC West 2022 (San Francisco, CA) 🇺🇸


"Enhance Trust with Machine Learning Model Error Analysis" (workshop)

August 18, 2022

Code PaLOUsa 2022 (Louisville, KY) 🇺🇸

Code PaLOUsa

"QA for AI systems" (talk)

June 30, 2022

ML Summit Munich 2022 🇩🇪

ML Summit

"Making Machine Learning Models Attack-Proof with Adversarial Robustness" (workshop)
"How XAI will quietly revolutionize AI" (keynote)

June 29, 2022

Geekle Data Science Global Summit 2022 🇺🇸🇪🇺


"The Data Science Journey: Myths and Tips" (talk)

June 14, 2022

WeAreDevelopers World Congress 2022 🇩🇪


"A.I. is NOT Software!" (talk)

May 3, 2022

DTC Online Event 🇩🇪


"Mitigating Bias with the XAI Toolbox" (workshop)

April 20, 2022

Data Science Salon Virtual 🇺🇸


"How XAI will quietly revolutionize AI" (talk)

April 19, 2022

ODSC East 2022 (Boston, MA) 🇺🇸


"Adversarial Robustness: How to make Artificial Intelligence models attack-proof!" (workshop)

March 26, 2022

WinHacks 2022 🇨🇦


"Starting your Data science Journey: Myths and Tips" (talk)

Feb. 8, 2022

AI Dev Conference @ DeveloperWeek 🇺🇸


"Making Machine Learning Models Attack-Proof with Adversarial Robustness" (tutorial)

Dec. 23, 2021

SuperDataScience Podcast #539 🇺🇸


"Interpretable Machine Learning" (podcast)

Dec. 4, 2021

Live Sessions by Experts 🇧🇪


"Mitigating Bias with the XAI Toolbox" (tutorial)

Nov. 13, 2021

Great North DevFest 2021 🇨🇦

Google Developer

"XAI is the new AI" (talk)
"PANEL: Responsible AI" (panel)

Nov. 5, 2021

WUR AI Workshop 2021 🇳🇱

Wageningen University & Research

"Crop stage forecasting with Machine Learning and Causal Inference" (talk & panel Q&A)

Nov. 3, 2021

Ai4 2021 Enterprise 🇺🇸


"Interpretable Machine Learning for Model Tuning" (lightning talk)

Oct. 13, 2021

AIExpo 2021 🇺🇸


"Path to Artificial General Intelligence" (panel)

Oct. 11, 2021

Global AI Conference 2021 🇺🇸

Global Big Data Conf

"What do Planes and Machine Learning have in common? How Interpretable ML can improve decision-making" (business talk)

Sep. 30, 2021

Strangeloop 2021 (St. Louis, MO) 🇺🇸


"Mitigating Bias in ML Models with Constraints" (workshop)

July 8, 2021

A.I. Access Series 🇯🇵🇺🇸 (in collaboration with Machine Learning Tokyo & KPMG Ignition Tokyo)

"XAI: Learning Fairness with Interpretable Machine" (keynote)

April 19, 2021

ML Summit Berlin 2021 🇩🇪

ML Summit

"Interpretable Multivariate Forecasting with Deep Learning" (workshop)

April 10, 2021

DataScienceGO Virtual April 2021 🇺🇸


"Fairness with Interpretable Machine Learning" (workshop)

April 8, 2021

Python for ML and AI Global Summit 2021 🇺🇸🇪🇺


"The Importance of Interpretable Machine Learning" (talk & panel Q&A)

March 24, 2021

Aggregate Intellect ML Interpretability Stream 🇨🇦


"XAI with Monotonic Constraints & Interaction Constraints" (keynote)

Feb. 27, 2021

Machine Learning Prague 2021 🇨🇿

ML Prague

"Ensuring Machine Learning Fairness with Monotonic Constraints" (workshop)

Feb. 20, 2021

DSGO Connect February 2021 🇺🇸


"The Importance of Incorporating Interpretation in Machine Learning Workflows" (keynote)

Oct. 30, 2020

Open Data Science Conference West 2020 🇺🇸


"Interpretable Machine Learning with Python: Interpreting & Improving Fairness for Recidivism Risk Assessments" (workshop)

Sep. 13, 2019

Strangeloop 2019 (St. Louis, MO) 🇺🇸


"Assistive Augmentation: Lip Reading with AI" (talk)

Sep. 6, 2019

8th Light University 🇺🇸


"Assistive Augmentation: Lip Reading with AI" (keynote)


My Projects
Smart Grid Short Term Load Forecasting with RNNs

Smart Grid Short Term Load Forecasting with RNNs

Deep Learning, Time Series
Audio-Assisted Lip-Reading System Using LSTM Artificial Neural Network

Audio-Assisted Lip-Reading System Using LSTM Artificial Neural Network

Computer Vision, Deep Learning, NLP, Transfer Learning
Haiku Generation with the GPT-2 language model

Haiku Generation with the GPT-2 language model

NLP, Transfer Learning
Predicting 3D Printer Failure

Predicting 3D Printer Failure

ETL, Interpretable ML, Machine Learning
3D Scanning Via Photogrammetry with Automated Stage

3D Scanning Via Photogrammetry with Automated Stage

Computer Vision
Data Quality Assessment on a Data Warehouse and KPI Assessment on Its Users

Data Quality Assessment on a Data Warehouse and KPI Assessment on Its Users

Descriptive Analytics, ETL
Interpretable Personal Loan Default Predictions

Interpretable Personal Loan Default Predictions

Deep Learning, Interpretable ML, Machine Learning
Chicago Hospital Ranking System

Chicago Hospital Ranking System

Descriptive Analytics, Geospatial / GIS
Predicting Rugby 7s Positions with Match-Play Statistics

Predicting Rugby 7s Positions with Match-Play Statistics

Geospatial / GIS, Statistical Learning
Estimating Driver Drowsiness & Distraction with Deep Learning & Computer Vision

Estimating Driver Drowsiness & Distraction with Deep Learning & Computer Vision

Big Data, Computer Vision, Decision Science, Deep Learning
A Regression-based Cross-Sectional Analysis of the Effects of Resources on Math Proficiency

A Regression-based Cross-Sectional Analysis of the Effects of Resources on Math Proficiency

Descriptive Analytics, Geospatial / GIS, Statistical Learning
Search Engine for Spontaneous and Serendipitous Decision Making

Search Engine for Spontaneous and Serendipitous Decision Making

Big Data, Decision Science, ETL, Geospatial / GIS, Machine Learning
Online Poker Analytics

Online Poker Analytics

Descriptive Analytics, ETL, Time Series


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