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Daniel Álvarez, MPA, FRM

New York, NY USA
LinkedIn
a l v a r e z (dot) d a (at) g m a i l (dot) c o m


My mission

To use my data science skills for social benefit to discover, diagnose and design responses to real-world problems of public interest. I believe that through collaboration and inclusive design, solutions can be developed to address complex, real-world challenges using data.

My current interests

  • I’m currently working on applying machine learning for geospatial anomaly detection and classification problems.
  • I’m currently learning about the application of transformers for real-world use cases.
  • I’m looking to collaborate on social good-minded projects.
  • I’m looking for help with geo-spatial analysis and building interactive dashboards showcasing model results on maps.

Work Experience

UNICEF, Office of Innovation - Frontier Technology Team

Innovation Manager/Data Science Lead
January 2024 - Present

Provide technical assistance to ideate, scope, develop and support implementation of data science, machine learning and artificial intelligence projects serving UNICEF’s programmatic objectives relating to humanitarian cash transfers, in-kind aid delivery, healthcare delivery, education technology, climate and sanitation. Facilitate and guide startups in developing mature data science applications in support of programmatic objectives with Regional and Country Offices. Support strategic hiring efforts of data science technical specialists and advisors, including all aspects of the candidate selection process.

UNICEF, Office of Innovation - Venture Fund

Data Science & Artificial Intelligence Mentor
October 2021 - March 2024 (Part Time)

Provide technical assistance in data science and artificial intelligence to UNICEF Venture Fund investments (start-up companies and country offices). Mentor companies one-on-one and provide them with technical advice through monthly calls and remote sprints. Support the Venture Fund in reviewing Data Science and Artificial Intelligence portfolio company repositories and validating they are in line with contractual requirements. Provide technical support for Data Science and Artificial Intelligence call for submissions, reviewing submissions to UNICEF Venture Fund.

Presidential Innovation Fellows, General Services Administration/Technology Transformation Services - US Geological Survey

Data Strategy Specialist
April 2023 - February 2024

Presidential Innovation Fellow detailed to the US Geological Survey (USGS) Energy and Minerals Resources Mission Area supporting mission-critical data strategy objectives around Presidential Administration priorities. Develop data strategies and methods to support activities of the Energy and Minerals Resources Mission Area. Collaborate on data management and data science efforts with internal stakeholders across the Minerals Resources Program and Energy Resources Program. Main projects included leading modernization of data project workflows by making scientific data more accessible and useful to USGS science researchers and the broader public, supporting USGS staff in the evaluation of proposals for the Critical Minerals Assessment using AI Support Challenge and integrating outputs into USGS infrastructure, and developing strategies with senior USGS staff on improving the management and delivery of geochemistry and geophysical data for the Earth Mapping Resources survey initiative.

Workday - Audit, Risk and Intelligence

Senior Data Scientist
May 2021 - April 2023

Develop machine learning models and data science tools for Workday Internal Audit, Risk and Intelligence activities. Collaborate on data science efforts with internal stakeholders in the Finance, People and Operations, Corporate Investigations and Internal Audit. Developed and deployed machine learning models to detect potential fraud and anomalies in expense claims data and time series forecasting of payroll tax quantities for internal Finance unit stakeholders.

World Food Programme, Cash-Based Transfers - Data Assurance Team

Data Scientist
March 2019 - May 2021

Executed critical data operations and analyses for the Cash-Based Transfers Program unit from World Food Programme (WFP) Headquarters. This included documenting, reviewing and processing structured and unstructured operational data on cash transfer programme interventions from World Food Programme country offices around the world. Developed and deployed data infrastructure and analytics solutions for WFP’s global cash transfer interventions to promote data assurance and accountability. Developed and implemented methodology to de-duplicate beneficiary identities in registration lists for cash assistance intervention programs. Created and implemented framework for the detection of anomalies in cash transfer programs in global crisis contexts. Supported WFP country offices in the training and recruitment of local data analysts. Represented the World Food Programme in coordinating committees on strategic partnerships with other United Nations agencies in global cash transfers operations. Pioneered data science efforts for global cash transfer operations of WFP.

United Service Automobile Association, Enterprise Stress-testing

Senior Quantitative Risk Analyst
January 2015 - March 2019

Executed strategic initiatives for critical regulatory compliance activities related to stress-testing at the Enterprise level. Devised scenarios for stress-testing, organized data validation efforts and performed risk analysis on stress-testing outcomes. Produced and evaluated credit and equity loss estimates for USAA’s investment portfolios. Evaluated forecasting models for estimating forward-looking losses for the company. Led Reverse Stress Testing efforts and quantified scenario impacts on the company.

Federal Reserve Bank of New York, Risk Group - Independent Price Verification

Risk Analyst
June 2013 - January 2015

Executed mission-critical deliverables as a function of the Federal Reserve Bank of New York’s Risk Group objectives. Performed independent price verification and valuation of collateral held at the Discount Window and assets held in the Federal Reserve Bank’s securities portfolios. Supported the Comprehensive Capital Analysis and Review (CCAR) system-wide stress-testing exercise by evaluating methodologies and models of supervised institutions.

Federal Reserve Bank of New York, Financial Institutions Supervision Group - Counterparty Credit Risk

Supervisory Risk Analyst
July 2009 - June 2013

Executed mission-critical deliverables as a function of the Federal Reserve Bank of New York’s safety and soundness objective. Performed counterparty credit risk assessments of regulated financial institutions and validated loss estimates to ensure alignment with risk standards. Led the Supervisory Modeling Team efforts to produce counterparty credit risk loss estimates for the Comprehensive Capital Analysis and Review (CCAR) systemwide stress-testing exercise. Contributed to regulatory examinations at systemically-important financial institutions on key credit and counterparty risks.

Education

School of Information, University of California, Berkeley, CA
Master of Information and Data Science

School of International and Public Affairs, Columbia University, New York, NY
Master of Public Administration, Advanced Policy and Economic Analysis

Brown University, Providence, RI
Bachelor of Arts, Economics and International Relations

Daniel Alvarez's Projects

ar-php icon ar-php

For of Ar-PHP (http://www.ar-php.org/project-php-arabic.html)

calendar-year icon calendar-year

This program prompts a user to specify a given four-digit year and then displays a calendar for each month in that year.

darts icon darts

Dynamic and Responsive Targeting System

deduplication-of-records icon deduplication-of-records

Deduplication of record linkage matching using biographical data and phonetics encoding in multiple languages

deep-learning-with-python-and-pytorch icon deep-learning-with-python-and-pytorch

Course material on developing Deep Learning models using PyTorch. The materials shows how to build and train deep neural networks—learning how to apply methods such as dropout, initialization, different types of optimizers and batch normalization. The course then focuses on Convolutional Neural Networks, training your model on a GPU and Transfer Learning (pre-trained models). The course ends with a focus on dimensionality reduction and autoencoders. Including principal component analysis, data whitening, shallow autoencoders, deep autoencoders, transfer learning with autoencoders, and autoencoder applications. This course was created by IBM.

docker-for-data-scientists icon docker-for-data-scientists

File repository for the Docker for Data Scientists course. `Docker` is a platform to develop, deploy, and run applications with containers. This is so that the application works on your computer or someone else's. This is important for reproducibility in data science/machine learning.

earth_ai_book_materials icon earth_ai_book_materials

The repo contains the source code, notebooks, and technical resources that assist students to read the book Artificial Intelligence in Earth Science.

fastbook icon fastbook

The fastai book, published as Jupyter Notebooks

gender_experiment icon gender_experiment

R Code and reporting output for randomized causal inference study to answer question on whether there is bias in following directions from gendered voices.

geokb icon geokb

Data processing workflows for initializing and building the Geoscience Knowledgebase

geowrangler icon geowrangler

🌏 A python package for wrangling geospatial datasets

idb_costaricapoverty_eda icon idb_costaricapoverty_eda

This Exploratory Data Analysis (EDA) took a Kaggle competition dataset sponsored by the Inter-American Development Bank (IADB) on predicting poverty levels amongst households in Costa Rica. The research idea is to identify and explore those variables (household attributes or assets found in homes) to understand the data structure and underlying relationships in the data.

islr icon islr

Introduction to Statistical Learning

lime icon lime

Lime: Explaining the predictions of any machine learning classifier

magasin icon magasin

Cloud native open-source end-to-end data / AI / ML platform

microsoft-planetary-computer icon microsoft-planetary-computer

The Planetary Computer combines a multi-petabyte catalog of global environmental data with intuitive APIs, a flexible scientific environment that allows users to answer global questions about that data, and applications that put those answers in the hands of conservation stakeholders.

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