Felice Rando, Developer in Seattle, WA, United States
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Felice Rando

Verified Expert  in Engineering

Data Scientist Developer

Location
Seattle, WA, United States
Toptal Member Since
June 15, 2021

Felice is an expert in machine learning, predictive analytics, and big data, having built dozens of solutions across multiple verticals, including finance, fraud, telecommunications, and pharmaceutical research, deriving business value from raw data. He has served both as a practitioner and analytics executive in organizations such as OECD, the University of Washington, First Data, ID Analytics, and multiple start-ups. Felice has a Master's degrees in theoretical and applied statistics.

Portfolio

DataRobot
DataRobot, Python, SQL, Spark SQL, Amazon Web Services (AWS)...
The Spur Group
Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT)...
Data Science Evolution
Bayesian Inference & Modeling, Data Mining, Jupyter Notebook, Python, SQL...

Experience

Availability

Part-time

Preferred Environment

Jupyter Notebook, Python, R, Slack, Visual Studio Code (VS Code), Statistical Analysis, ETL, Regression

The most amazing...

...tool I've built was a series of models that improved the lives of millions of telecommunications customers by creating more strategic, personalized offers.

Work Experience

Customer Facing Data Scientist

2021 - 2023
DataRobot
  • Helped dozens of clients achieve AI success using DataRobot and other AI tools, developing and working through use cases, explaining AI/ML concepts and working through data issues.
  • Led and coordinated with distributed external teams to create machine learning models, from raw data to final deployment, monitoring, and automated retraining.
  • Collected customer feedback related to feature enhancements and requests on the DataRobot platform.
  • Built complex time multi-series models with thousands of variable-length individual series.
Technologies: DataRobot, Python, SQL, Spark SQL, Amazon Web Services (AWS), Artificial Intelligence (AI), Time Series, Anomaly Detection, Clustering, Data Science, Machine Learning, Data Analysis, Financial Modeling, Frameworks, Analytics, ETL, Regression, Backtesting Trading Strategies, Hedge Funds, Forecasting, Snowflake, Transportation & Logistics, Statistical Data Analysis, Predictive Modeling, Finance

Data Scientist

2021 - 2021
The Spur Group
  • Built binary and multi-class NLP models to determine violations for vitamin supplement reviews.
  • Explored the entire history of available data to select the most appropriate training and validation data sets.
  • Uncovered data issues relating to change in business practices and explained how this relates to model performance.
Technologies: Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Keras, Artificial Intelligence (AI), Data Science, Machine Learning, Statistical Analysis, Data Analysis, Frameworks, NumPy, Analytics, ETL, Regression, Real Estate, Statistical Data Analysis, Trend Analysis, Predictive Modeling, Algorithms, Data Matching

Principal Data Scientist

2014 - 2021
Data Science Evolution
  • Modeled compensation distributions, specifically 5th, 25th, median, 75th, and 95th percentiles, using Bayesian Networks on limited data fields, exceeding the performance of a much more complicated champion model.
  • Built second-party fraud models in the automotive lending space on behalf of lenders, uncovering bad dealerships and other actors.
  • Used health insurance claims data to understand the effects of new pharmaceuticals in the pharmaceuticals marketing space.
  • Created semi-supervised segmentation for health care professionals based on their propensity to prescribe newer treatments.
Technologies: Bayesian Inference & Modeling, Data Mining, Jupyter Notebook, Python, SQL, Artificial Intelligence (AI), Data Science, Machine Learning, Statistical Analysis, Data Analytics, Data Visualization, Data Analysis, Data Scraping, Analytics, ETL, Regression, Web Scraping, Statistical Data Analysis, Trend Analysis, Predictive Modeling, Algorithms, Data Matching

Lead Data Scientist

2016 - 2018
SteppeChange
  • Built a series of solutions for customer personalization in the telecommunications space for a large EU telecommunications client, serving 20 million customers.
  • Led several parallel use cases in the course of the data management platform project with terabytes of daily data.
  • Created models to determine the probability of a customer exceeding their voice, text, and data allotments. Incremental spend expectation once this threshold was met was exceeded.
  • Analyzed whether a customer was rationing their usage to manage their allotted thresholds.
  • Used the outputs of the models and other data to create the next best offer recommendations and pricing, keeping in mind the overall impact on the business.
Technologies: Python, Apache Hive, Spark SQL, Customer Segmentation, CatBoost, XGBoost, Scikit-learn, Artificial Intelligence (AI), Data Science, Machine Learning, Data Analytics, Data Visualization, Data Analysis, Frameworks, NumPy, Analytics, ETL, Regression, Statistical Data Analysis, Trend Analysis, Predictive Modeling, Data Matching, Pricing

VP, Data Science

2011 - 2014
LexisNexis Risk Solutions
  • Built and led a team of highly skilled data scientists and engineers to solve challenging problems for well-known financial, retail, and wireless institutions.
  • Collaborated with the product organization to develop next generation credit risk and fraud models and attributes, seeking to derive incremental and unique value from internal data sources combined effectively with third-party data.
  • Advanced research and development efforts, including cutting-edge custom modeling algorithm development, creation and adaptation of new data sources, and developing new verticals.
  • Created a proof-of-concept to uncover Federal and State tax identity fraud to combat a multimillion-dollar problem where bad actors were using other people's personal identities to get returns sent to them.
  • Calculated the Customer Lifetime Value and built attrition models on LifeLock customers.
Technologies: Fraud Prevention, Credit Risk, Management, Leadership, Mentorship & Coaching, Team Building, Executive Presentations, Artificial Intelligence (AI), Data Science, Machine Learning, Data Analysis, Analytics, ETL, Regression, Backtesting Trading Strategies, Statistical Data Analysis, Trend Analysis, Predictive Modeling, Finance, Retail & Wholesale

Director, Analytics

2006 - 2010
First Data
  • Led custom analysis in the financial services space, building client-specific models and taking the problem from raw data to the final solution, anticipating business requirements and suggesting alternative approaches when necessary.
  • Created marketing strategies for a top, frequent flier loyalty program, building consumer clusters and models looking to predict response to a given promotion, designing experimental campaigns, and scoring prospects to optimize new offers.
  • Solved a payment reversal risk model problem by applying outside-the-box thinking on a subprime portfolio at a mid-sized US bank. Saved the client a minimum of $2.4 million per year, and provided the breakthrough in our sales process.
  • Built a solution for a top 10 US bank in the fraud space that enabled our client to determine which credit card numbers may have been compromised using a skimming device at a gas pump.
  • Collaborated with software engineers in developing, prototyping, and integrating state-of-the-art statistical and machine learning algorithms into enterprise analytics software.
Technologies: Machine Learning, Consulting, Team Leadership, Information Retrieval, Cluster Analysis, Predictive Analytics, Text Analytics, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), Marketing Analytics, Artificial Intelligence (AI), Data Science, Statistical Analysis, Data Analytics, Data Visualization, Data Analysis, Analytics, ETL, Regression, Backtesting Trading Strategies, Statistical Data Analysis, Trend Analysis, Predictive Modeling, Finance, Retail & Wholesale

Predict the Likelihood of Exceeding Mobile Phone Allowances

I built a model for telecommunication marketing to predict the likelihood of an individual cell phone user to exceed their threshold at an early point of the 30-day billing cycle, scorable at any point in the cycle (including very early in the cycle, before day ten), to be able to make a new offer or an add-on offer to the customer. Significant time-aware feature engineering was used, and the models were largely built in XGboost.

Review flagging

Built a Keras/NLP model to determine whether a product review on nutritional supplements should be flagged for various violations, including profanity and low quality reviews and/or medical advice on a challenging dataset containing multiple changes in business practices.

Compensation Analysis

Used a Bayesian Network to model the distribution of various components of compensation (specifically salary and bonus) using limited inputs, such as geographic location, years of experience, job function and title, and education. Returned a distribution of which the 5/25/median/75/95 percentiles were of interest to the end user.

Customer Loyalty and Segmentation

Examined and analyzed the distributions of two major loyalty programs that were to be combined during a merger to ensure data integrity, reduce overlap, and proposing customer segments. Gave feedback to the engineering teams regarding data transformations to render the distributions as equivalent as possible. Built models looking to predict probability of response to a given promotion, designing experimental campaigns, and scoring prospects in order to prioritize work and optimize new offers.

Fraud Prevention

Built solutions to determine identity fraud during consumer applications. Created features out of interconnected applications using identity elements across a consortium of lenders and service providers including banks, utilities and telcos. Identified and evaluated third party data providers to provide additional lift to models. Used data to create secondary models such as orthogonal credit scores, and to create additional proofs of concepts such as identifying tax fraud and fraud rings.

Attrition

Built solution identifying required data and models to determine customer subscription churn for a customer in the identity theft prevention space. Combined results with estimation of customer value to route inbound calls to appropriate level customer service representatives.
2012 - 2013

Postgraduate Certificate in Executive Perspective for Scientists and Engineers

University of California - San Diego, California, USA

1999 - 2001

Master's Degree in Applied Statistics

Macquarie University - Sydney, Australia

1992 - 1998

Master's Degree in Statistics

La Sapienza - Rome, Italy

AUGUST 2023 - PRESENT

Prompt Engineering for ChatGPT

Coursera

Libraries/APIs

XGBoost, Keras, CatBoost, Scikit-learn, NumPy, TensorFlow

Tools

AutoML, DataRobot, Slack, Microsoft Teams, Spark SQL

Paradigms

Data Science, ETL, Management, Anomaly Detection

Languages

SQL, Python 3, Python, Snowflake, SAS, R

Industry Expertise

Telecommunications, Marketing, Retail & Wholesale

Platforms

Jupyter Notebook, Amazon Web Services (AWS), Visual Studio Code (VS Code)

Storage

Apache Hive

Other

Logistic Regression, Statistics, Cluster Analysis, Machine Learning, Predictive Analytics, Data Mining, Gradient Boosted Trees, Explainable Artificial Intelligence (XAI), Artificial Intelligence (AI), Statistical Analysis, Data Analytics, Data Analysis, Analytics, Regression, Statistical Data Analysis, Predictive Modeling, Generalized Linear Model (GLM), Time Series Analysis, Natural Language Processing (NLP), Customer Segmentation, Fraud Prevention, Text Analytics, Marketing Analytics, Big Data, Churn Analysis, Data Visualization, Loyalty Management, Customer Lifetime Value (CLV), Random Forests, Customer Research, Generative Pre-trained Transformers (GPT), Real Estate, Backtesting Trading Strategies, Forecasting, Trend Analysis, Experimental Design, Multivariate Statistical Modeling, Principal Component Analysis (PCA), Probability Theory, Statistical Methods, Bayesian Inference & Modeling, Applied Mathematics, Executive Presentations, Credit Risk, Leadership, Mentorship & Coaching, Team Building, Consulting, Team Leadership, Information Retrieval, Time Series, Feature Engineering, Loyalty Programs, Compensation, Sentiment Analysis, Reviews, Feature Analysis, Health Insurance, Fraud Investigation, Decision Trees, Financial Services, Churn Management, Customer Data, Clustering, Pharmaceuticals, Monte Carlo Simulations, Financial Modeling, Frameworks, Data Scraping, Hedge Funds, Web Scraping, Prompt Engineering, Transportation & Logistics, Finance, Algorithms, Data Matching, Pricing

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