Felice Rando
Verified Expert in Engineering
Data Scientist Developer
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
Experience
Availability
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
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.
Data Scientist
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.
Principal Data Scientist
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.
Lead Data Scientist
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.
VP, Data Science
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.
Director, Analytics
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.
Experience
Predict the Likelihood of Exceeding Mobile Phone Allowances
Review flagging
Compensation Analysis
Customer Loyalty and Segmentation
Fraud Prevention
Attrition
Skills
Languages
SQL, Python 3, Python, Snowflake, SAS, R
Paradigms
Data Science, ETL, Management, Anomaly Detection
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, Time Series Analysis, Natural Language Processing (NLP), Customer Segmentation, Text Analytics, Marketing Analytics, Big Data, Churn Analysis, Data Visualization, Loyalty Management, Customer Lifetime Value, Random Forests, Customer Research, GPT, Generative Pre-trained Transformers (GPT), Real Estate, Backtesting Trading Strategies, Forecasting, Experimental Design, Generalized Linear Model (GLM), Multivariate Statistical Modeling, Principal Component Analysis (PCA), Probability Theory, Statistical Methods, Bayesian Inference & Modeling, Applied Mathematics, Executive Presentations, Fraud Prevention, 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
Tools
AutoML, DataRobot, Slack, Microsoft Teams, Spark SQL
Libraries/APIs
Keras, CatBoost, XGBoost, Scikit-learn, NumPy, TensorFlow
Platforms
Jupyter Notebook, Amazon Web Services (AWS), Visual Studio Code (VS Code)
Storage
Apache Hive
Industry Expertise
Telecommunications, Marketing
Education
Postgraduate Certificate in Executive Perspective for Scientists and Engineers
University of California - San Diego, California, USA
Master's Degree in Applied Statistics
Macquarie University - Sydney, Australia
Master's Degree in Statistics
La Sapienza - Rome, Italy