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Nicole L. Roberts New York, NY ***** • 917-***-**** • ad0xma@r.postjobfree.com

nicolelroberts.com • linkedin.com/in/nicolerobertsdatascientist • github.com/ellenicoleroberts Summary

• Former entrepreneur turned Data Scientist with a retail focus excelling at quantitative business decision making.

• Approaches problem solving from an innovative and lean, business-centric perspective.

• Frequently assumes a leadership role on projects due to strong communication and collaboration skills.

• Achievements include creating machine learning models to predict a retail customer’s discount affinity (to offer customized coupons) with up to 92% accuracy.

• Passionate about making discoveries with predictive analytics and telling stories with data to improve data- driven decision making.

Skills

Languages and Frameworks: Python, SQL, Flask, HTML/CSS, Solidity, JSON Applications: AWS, JupyterLab, BigQuery, Postgresql, Salesforce CRM, Adobe Analytics, GitHub, Tableau, Excel Methods and Tools: Machine Learning (Supervised, Unsupervised, Neural Networks), Time Series Forecasting, Statistical Modeling, Scikit learn, TensorFlow, Pandas, NumPy, Seaborn, SQLAlchemy Education

Ira A. Fulton Schools of Engineering at Arizona State University • Tempe, AZ Bachelor of Science in Industrial Engineering

Professional Certifications and Training

FinTech Boot Camp Cert #58486438 Columbia Engineering August 25, 2022 Data Science Projects

Modeling the VIX with LSTM • bit.ly/3STq0M7 August 2022 Project Leader, Head Data Scientist

Various multivariate, multistep LSTM models using SPX options data to forecast the CBOE VIX to improve future market volatility forecasts. Monte Carlo simulation and Facebook Prophet forecasts included for comparison. Predicting ROI for Option Contracts with Machine Learning • https://bit.ly/3TUN1Q4 July 2022 Project Leader, Head Data Scientist

Predicts the percent profit or loss of weekly call and put option contracts at two days until expiration using machine learning; features include option contract variables along with VIX prices, inflation, and various sentiment metrics. Professional Experience

American Century Investments • New York, NY (full time, hybrid) May 2023 – Present Marketing Data Developer

• Create and maintain customized scripts and ETL jobs to power reports, dashboards, predictive models, and ad hoc analysis.

• Identify, design, and implement internal process improvements including automating manual Page 2/2

processes, optimizing data delivery, and re-designing infrastructure.

• Partner with internal teams to map new and existing marketing data sources, as well as provide accompanying documentation and data governance policy.

• Assist in the design, build, and maintenance of the marketing data lake. Express • Columbus, OH (contract, remote) December 2022 – May 2023 Data Scientist

• Worked closely with Marketing, Finance, and Allocation teams to understand business needs and develop innovative strategies and operational improvements through data-driven methods.

• Performed extensive feature engineering using SQL for machine learning models based on a thorough understanding of retail customer behavior and end-goal use cases.

• Developed a two-part machine learning model that predicts what discount amount a member generally requires to make a purchase. When applied to Marketing’s campaign outreach, model boosts revenue by eight million dollars.

• Built and automated a Customer Segmentation Map based on a member’s monetary and frequency shopping patterns that feeds into a machine learning model predicting customer segment migration patterns.

• Increased revenue by four million dollars by isolating and excluding from Marketing campaign offers customers with a high return rate and low customer lifetime value score. OptyX • New York, NY December 2021 – December 2022 Data Scientist

OptyX is a startup financial services solution predicting the outcome of an option contract’s return on investment.

• Developed a proprietary machine learning pipeline using a set of option contract variables as features along with, notably, predicted VIX prices (derived from aforementioned ‘Modeling the VIX with LSTM’ project model).

• Provided traders confidence in their investment decisions, accurately predicting ROI outcome with up to 77% accuracy and 85% precision.

• Optimized model parameters with routine back testing.

• Developed additional predictive technical indicators to identify optimal entries and exits. Simple the App • New York, NY November 2020 - December 2021 Web Developer

Simple the App is a cloud-based field service management SaaS solution that streamlines internal business processes through both a mobile app and desktop dashboard.

• Architected and programmed CRM system via a Flask web framework and Jinja2 template engine with PostgreSQL, including real-time scheduling and dispatching through an automated app-to-technician SMS text messaging system, expense tracking, and technician-client reminders.

• Drastically reduced the time it takes companies to assign and manage workload and maintain organized, up- to-date accounting; beta version testing thus far implies the following metrics: o Freed up ~82% of dispatcher’s time by: (1) automating input process of incoming leads, (2) streamlining job assignment process, job tracking, and communication via smart user-designed interface with messaging, and (3) offloading ~80% of jobs to an automated system. o By dramatically reducing dispatcher’s workload per technician, permitted companies to increase their number of technicians by up to 7-fold, thereby increasing jobs and revenue up to ~700%. o Eliminated over 90% of bookkeeping workload with automated expense tracking and accounting.

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