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About Us

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OUR STORY

Factored was conceived in Palo Alto, California by Andrew Ng and a team of highly experienced AI researchers, educators, and engineers to help address the significant shortage of qualified AI and machine learning engineers globally. ​

We know that exceptional technical aptitude, intelligence, communication skills and passion are equally distributed around the world, and we are committed to testing, vetting and nurturing the most talented engineers on behalf of our clients.

OUR MANAGEMENT TEAM

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DR. ANDREW NG

Founding Advisor

Andrew is a computer scientist, investor, writer and adjunct professor at Stanford University. Andrew co-founded Google Brain, Coursera and Deeplearning.ai as well as spearheading the AI Fund, a team of pioneers, entrepreneurs, and venture capitalists working to solve significant societal challenges using AI.

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ISRAEL NIEZEN

CEO

Israel is experienced in leading high-performance teams and accelerating growth in enterprise technology. He is passionate about data science and believes in the power of education and technology to improve lives. He holds an MBA from Harvard Business School.

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LUZ CAMARGO

VP of Finance

Luz is a highly proficient finance professional. She has international experience in FP&A, investor relations, and treasury and reporting. She has lived and worked across the Americas and Asia and holds an MBA from the University of Maryland’s Robert H. Smith School of Business.

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KASSANDRA LA RIVA

Director of Business Operations

Kassandra has experience working in cross-functional, international teams at the forefront of innovation in the tech, telecom and education industries. She holds a Bachelor’s in Economics from Columbia University.

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JUAN DAVID GIL

Tech Lead & Machine Learning Engineer

Juan David began his career building computer vision solutions for intelligent transportation. He has also worked on diverse engineering projects involving CRM and Customer Analytics. He has significant formal training in AI and Machine Learning, and holds an MSc in Computer Science.

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VIVIANA TABORDA

Operations Manager

Viviana is an experienced operations and finance professional who has worked in various countries across Latin America including Colombia, Brazil and Mexico. She holds a Bachelor’s in Business Administration from EAFIT University in Colombia and an MBA from Università di Pisa in Italy. 

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LINA BARRERA

Recruitment Business Partner

Lina is an experienced recruiter and general HR professional. She has experience recruiting in the tech industry for roles across the Americas. She holds a Bachelor’s in Industrial Engineering from the Pontificia Universidad Javeriana in Bogotá, Colombia.  

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LAURA FIELD

Communications Manager

Laura is an experienced communicator and PR professional who’s worked on communications campaigns for a variety of SMEs across the globe. She holds a Bachelor’s in Modern Languages from the University of Oxford. 

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OUR TEAM

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GET IN TOUCH

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    We are committed to vetting, selecting and upskilling the most talented engineers for our program and on behalf of our clients.
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    CONTACT US

    • +1-650-353-5484
    • sales@factored.ai
    • 195 Page Mill Road,
      Suite 115
      Palo Alto, CA 94306

    NAVIGATION

    © Copyright Factored 2019 - 2021. All Rights Reserved.

    FINTECH SOLUTIONS

    Identity Fraud Detection
    for Credit Card Users

    Detecting Identity Fraud
    in Credit Card Users

    The Challenge:

    To create a model that could improve identity fraud detection in credit card users while maintaining variable explainability. 

    The Factored Solution:

    We implemented an experimentation pipeline to detect the best model given a set of variables. A deep analysis of variables was established, which improved model accuracy. 

    The Outcome:

    The detection rate for cases of identity fraud improved by 7.5%, which saved our client time and money as they no longer had to spend on or carry out additional processes.

    We used a variety of skills and tech stack tools including: TensorFlow, Python, LGBM, XGBoost, Hyperopt, Deep Learning, Fraud Detection, Forecasting, and Model Interpretability. 

    Next Project

    FINTECH SOLUTIONS

    Credit Card Default Model
    with Deep Learning

    Using Deep Learning to Build
    a Credit Card Default Model

    The Challenge:

    To create better models for credit card default prediction among current clients, without needing to utilize any additional features other than the ones already available.

    The Factored Solution:

    We used deep learning models to encode temporal features that classic approaches could not facilitate during optimization. 

    The Outcome:

    We created a more accurate representation of customers who’d been using the product for more than 6 months (50% of users), and reduced the effort required for feature engineering. 

     

    We used a variety of skills and tech stack tools including: TensorFlow, Python, Deep Learning, RNNs, Forecasting, and Credit Scoring. 

    Previous Project

    Next Project

    FINTECH SOLUTIONS

    Creating Alternative Credit
    Scoring Methods

    Providing Innovative Credit
    Scoring Methods

    The Challenge:

    Traditional credit scoring requires clients to have a credit history, and when they don’t they are either denied their applications or are given excessive interest rates. The challenge here was to build an alternative credit scoring system for small and medium enterprises (SMEs) to assess and understand their own financial health. 

    The Factored Solution:

    With a combination of machine learning and credit risk expertise, we built a set of models that allowed us to measure the credit health of SMEs using only transactional data from their bank accounts.

    The Outcome:

    We built a scalable web application for SMEs to link their bank accounts and ultimately monitor their own credit health.

     

    The tech stack and skills we used to implement this include: PostgreSQL, AWS, Docker, Celery, Jenkins, GraphQL, Vue.js, Microservice-Oriented Architectures, APIs, Containerization, and CI/CD.

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