Daniel Yinanc, Developer in Toronto, ON, Canada
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Daniel Yinanc

Verified Expert  in Engineering

DevOps Developer

Location
Toronto, ON, Canada
Toptal Member Since
February 6, 2019

Daniel is a data scientist and AI/ML engineer with over a decade of experience in the field, mainly specializing in data science and machine learning domains with a particular emphasis on building high-security ML platforms. He has worked for over a decade as a data scientist, application security architect, and principal consultant specializing in data science, cybersecurity, and machine learning.

Portfolio

Daniel Yinanc Inc.
Machine Learning, Artificial Intelligence (AI), Data Management Platforms, ETL...
Rainmaker Technologies Inc.
Python, Data Science, DataOps, Data Engineering, Machine Learning, Databricks...
Mawer
Python, DataOps, Data Science, Machine Learning, Deep Learning, AWS IoT...

Experience

Availability

Full-time

Preferred Environment

Data Science, Machine Learning, DevSecOps, Machine Learning Operations (MLOps), DataOps, Application Security, Cloud, Cybersecurity

The most amazing...

...thing I've built was a security framework for an entire industry (robotic process automaton) while working with Automation Anywhere.

Work Experience

Principal Data Scientist | Principal ML Engineer | Principal Application Security Architect

2018 - PRESENT
Daniel Yinanc Inc.
  • Envisioned and executed the development of the Robotic Process Automation (RPA) industry's 1st and only application security framework.
  • Gave presentations as a keynote speaker, built partnerships with cybersecurity firms, and designed a certification program.
  • Led and launched Asgard, a high-security machine learning (ML) data platform encompassing artificial intelligence (AI), MLOps, data analysis, data visualization, and data custodianship capabilities with a FAANG firm.
  • Developed an anomaly detection system for detecting faulty requests, employing a KNN clustering algorithm, using Caret with high accuracy, and reducing operator waste time by over 80%.
  • Created a chatbot utilizing NLP via Rasa to reduce human intervention by 50% on Slack support channels for a FAANG firm.
  • Architected and developed FleetView, a star-schema data warehouse combining information from 40+ services in a single pane of glass that reduced server assignment SLAs from 3+ weeks to less than a day.
  • Built a DevOps platform for a robotic process automation firm, ensuring secure bot development via machine learning algorithms and rule-based scanning techniques.
  • Designed and launched Olympus, a high-security machine learning platform encompassing big data, artificial intelligence, DataOps, MLOps, data analytics, and data visualization for a major recruitment platform.
Technologies: Machine Learning, Artificial Intelligence (AI), Data Management Platforms, ETL, Data Governance, Dashboards, Big Data Architecture, Deep Learning, Data Science, Databricks, Python, Amazon SageMaker, PostgreSQL, REST, Docker, Cloud, DevSecOps, Application Security, Generative AI, Large Language Models (LLMs), Azure Databricks

Lead Data Scientist

2014 - 2018
Rainmaker Technologies Inc.
  • Brought in to jump-start external clients' startup products and get them up and running in data science, machine learning, eCommerce, and enterprise products domains.
  • Conceptualized and launched Raincheck, a coupon sales platform competing with LivingSocial and Groupon, in less than six months as MVP, recovering the product from a failed product launch by prior management.
  • Directed product design and development for ChatFish, an AI chatbot platform designed to be white-labeled and marketed to realtors and medical professionals, leading to a successful product launch.
  • Managed strategic product marketing efforts for an oil and gas startup, which created a novel refining technology, reducing the carbon footprint of the refining process by over 50%.
  • Oversaw product design and development efforts for a recommendation engine targeting the telecommunications industry's online checkout process that led to a 20% decrease in abandoned shopping carts.
  • Built and led product, design, and data science teams supporting all machine learning, data strategy, and product design with a culture focused on tight collaboration, transparency, and mentoring within the work hard/have fun framework.
Technologies: Python, Data Science, DataOps, Data Engineering, Machine Learning, Databricks, AWS IoT, Cloud, Artificial Intelligence (AI)

Senior Data Scientist

2011 - 2014
Mawer
  • Built a customer attrition random forest model for wealth management clients and improved monthly retention by 50 basis points for clients likely to change houses by providing timely support calls.
  • Partnered with a portfolio support team to identify customer segments to target using a K nearest neighbors (KNN) clustering algorithm, improving cold call conversion rate by over 30%.
  • Compiled and analyzed data surrounding equity and fixed-income security prices, seeking anomalies for potential mispricing opportunities leading to over $10 million in trades.
  • Implemented time series analysis to develop equity benchmarks to compare with internal equity models used in asset management, leading to reduced benchmark deviations and greater fund performance.
  • Served as a data custodian in charge of corporate data integrity and access security for security pricing and fund performance data.
Technologies: Python, DataOps, Data Science, Machine Learning, Deep Learning, AWS IoT, Artificial Intelligence (AI)

DevOpsicon

https://github.com/devopsicon
The purpose of the DevOpsicon organization is to demonstrate, for the benefit of open source community, the best practices involved in DevOpsifying a realistic app with realistic components:

• Rest microservices (sales, expenses, and users)
• Customer-facing (hybrid mobile app)
• Message bus microservices (files)
• Tool agnosticity (all build tools can do the job)
• Language agnosticity (all languages can do the job)

Eiffel

https://eiffel-community.github.io/
The Eiffel protocol enables technology-agnostic communication for CI/CD ecosystems. Eiffel is based on decentralized real-time messaging, providing traceability and KPIs across your pipelines and platforms.
2023 - 2024

Master's Degree in Data Science

Harvard University - Boston, MA, USA

2005 - 2007

International Master of Business Administration (MBA) in Innovation and Business

Schulich School of Business - Toronto, ON, Canada

2001 - 2005

Bachelor of Science Degree in Mathematics and Physics

Rutgers University - New Brunswick, NJ, USA

Libraries/APIs

React, TensorFlow, Keras, Scikit-learn, Node.js, NumPy, Pandas, Natural Language Toolkit (NLTK)

Tools

Jenkins, Travis CI, CircleCI, RabbitMQ, Gradle, Git, Apache Maven, GIS, Mercurial, Terraform, Amazon SageMaker

Industry Expertise

Cybersecurity

Languages

Java, JavaScript, Scala, Erlang, SQL, Python, Eiffel

Paradigms

Unit Testing, Functional Testing, DevSecOps, DevOps, Data Science, ETL, Functional Programming, Concurrent Programming, Test-driven Development (TDD), Behavior-driven Development (BDD), Object-relational Mapping (ORM), Microservices, Penetration Testing, REST

Frameworks

Spring Boot, Express.js, React Native, Akka, AngularJS, Hadoop, Spark, Django, Angular, Koa

Storage

MySQL, MongoDB, Elasticsearch, Microsoft SQL Server, Redis, Google Cloud, NoSQL, Sybase, PostgreSQL, Neo4j

Platforms

Kubernetes, Docker, VMware Tanzu Application Service (TAS) (Pivotal Cloud Foundry (PCF)), OpenStack, Azure, Jakarta EE, Android, Talend, Amazon EC2, Amazon Web Services (AWS), Databricks, AWS IoT

Other

Machine Learning, AWS DevOps, Cloud, Pipelines, Performance Testing, APM, Application Security, Information Security, Security, IT Security, Cloud Services, DataOps, Azure Databricks, Agile Software Testing, Static Application Security Testing (SAST), Dynamic Application Security Testing (DAST), Security Audits, Relational Database Services (RDS), API Gateways, Artificial Intelligence (AI), Boot, Tornado, Computer Vision, Natural Language Processing (NLP), Recommendation Systems, Market Segmentation, Graphs, Optimization, Data Management Platforms, Data Governance, Dashboards, Big Data Architecture, GPT, Generative Pre-trained Transformers (GPT), Deep Learning, Large Language Models (LLMs), Generative AI, Machine Learning Operations (MLOps), CI/CD Pipelines, Projects, Data Engineering

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