Series 7 & 63 Licensed Stockbroker, Dedicated Active Trader Team @ Charles Schwab
Customer Engineer, Google Cloud @ Google
Master of Science (M.S.), Business Analytics @
University of Colorado Denver
I am a Data Scientist working at Deloitte's VizStudio. Previously, my background has been in marketing analytics. I have been trained to think strategically about marketing attribution,campaign response modeling, forecasting, and optimizing marketing investments. I have extensive experience building quantitative models in R and Python, and executive-level dashboards that visualize my findings using Tableau. I am skilled
I am a Data Scientist working at Deloitte's VizStudio. Previously, my background has been in marketing analytics. I have been trained to think strategically about marketing attribution,campaign response modeling, forecasting, and optimizing marketing investments. I have extensive experience building quantitative models in R and Python, and executive-level dashboards that visualize my findings using Tableau. I am skilled in the use of R, SAS, Stata, Python, Microsoft SQL Server, PostgreSQL, MongoDB, Excel, and Tableau.
▪ predictive modeling
▪ customer segmentation
▪ clustering and classification
▪ machine learning
▪ data mining using SQL/Python
▪ text mining and topic modeling
▪ quantitative forecasting
▪ data visualization and dashboarding
▪ database marketing
▪ campaign response measurement & design
▪ marketing strategy
▪ marketing attribution and media-mix modeling
▪ statistics and distribution modeling
▪ distribution modeling using R
▪ social network analysis
▪ market-basket analysis
▪ pricing optimization
Quantitative forecasting and modeling techniques:
▪ Machine Learning techniques
▪ linear programming and optimization (Simplex Method)
▪ linear/logistic regression
▪ multiple regression
▪ ordinary least squares (OLS) forecasting models
▪ Holt-Winters method (for building seasonality coefficients)
▪ Bass Diffusion Model (for forecasting new product sales)
▪ Bayes’ Theorem
▪ decision trees
▪ Markov Chain processes
▪ K-means clustering
▪ Attendee of the 2014 Annual Tableau Conference in Seattle, WA (Sept. 2014)
▪ Avid and informed reader of literature involving marketing science, data science, and applied analytics
▪ Triathlete, Ironman competitor, marathon runner, skier, golfer, and tennis player
▪ Qualified for The 2015 Boston Marathon during the Denver Rock N’ Roll Marathon (10/20/2013)
Data Scientist, Deloitte VizStudio @ Data Science Projects:
▪ Built an optimization model in R that uses the Simplex Method algorithm to optimize a supply chain by matching suppliers for each SKU based on a number of different attributes. The R optimization model was fully integrated with a front-end Tableau dashboard to allow business users to interact with and visualize the outcome of different scenarios
▪ Used Python to scrape Twitter data from the API for a social media sentiment analysis
▪ Employed advanced text mining and topic modeling methods on survey data, and built a corresponding Tableau dashboard to visualize the results
▪ Conducted a Market-Basket Analysis (MBA) for client project using R From June 2015 to Present (5 months) Data Scientist, Strategic Marketing Analytics @ ▪Accountable for generating the demand forecast of $500MM marketing organization
▪Responsible for providing data-driven, marketing-based analytic consulting to C-level executives
▪Built a series of marketing-mix models used to evaluate the effectiveness of individual marketing tactics and optimize the organization’s investment strategy. Models frequently employed advanced quantitative modeling techniques (see “Skills”)
▪Used R to develop an ordinary least squares (OLS) model to generate demand coefficients for forecasting daily sales call volume. Coefficients were optimized for each variable that impacts sales calls such as day-of-the-week, holidays, seasonality, marketing investments, competitor promotions, etc. By accounting for known variables, the model calculated the net effect of simultaneous variables and accurately forecast daily sales call volume for staffing purposes and media planning. Coefficients were optimized by minimizing the sum of squared absolute error between actual sales calls vs. forecast.
▪Completed various competitive intelligence analyses including quantifying the subscription losses and economic impact of a competitor's promotional pricing
▪Forecast and quantified the expected subscriber base lift Dish could expect to receive as the exclusive provider of the SEC and Pac-12 Networks among major competitors
▪Developed several executive-level Tableau dashboards that integrate forecasts and business performance metrics contained in various SQL tables for effective data visualization (attendee of the 2014 Annual Tableau Conference in Seattle, WA)
▪Built a model that forecasts sales call volume generated by 170+ simultaneous Direct Mail campaigns which are all in different stages of their campaign lifecycle and drive varying volume based on different creative messaging and targeted demographics
▪Provided ongoing support for direct response campaign strategy development, campaign test, measurement design, and post-campaign analysis From June 2013 to June 2015 (2 years 1 month) Englewood, COSeries 7 & 63 Licensed Stockbroker, Dedicated Active Trader Team @ ▪Promoted to the Dedicated Active Trader Team- Summer 2012
▪Successfully Passed Series 7 & 63 Stockbroker examinations in first attempts (Series 7 exam is a 6 hour, 250 question exam)
▪Zero trading errors during entire tenure as an advanced broker.
▪Routinely outperformed peers in all measurable metrics including: net new company assets, brokerage account sales, call handle time, customer satisfaction surveys, and demonstration of client education resources.
▪Cross-trained to support Charles Schwab Bank inquiries
▪Placed trades and frequently discussed investment products, including common and preferred stock, options, bonds, mutual funds, exchange-traded funds (ETFs), closed end funds, real estate investment trusts (REITS), annuities, limited partnerships, American depository receipts (ADRs), foreign ordinaries, and CDs.
▪Educated clients about basic trading mechanics such as order types (market orders, limit orders, stop orders, etc.), extended hours and pre-market trading sessions, margin loans, minimum equity maintenance requirements on securities, the inside market bid/ask spread, IPOs, and penny-stock trading rules.
▪Informed clients of the tax implications of investing as it relates to their cost basis, realized or unrealized gains/losses, retirement accounts, tax forms, wash sales, gifted or inherited securities, phantom interest, and accretion/amortization.
▪Analyzed the effects of complex corporate actions such as: stock splits, reverse splits, spinoffs, cash/stock mergers, bankrupt securities, tender offers, rights offerings, and cash/stock dividends.
▪Explained the inverse relationship between bond yields and outstanding bond prices, and the risks involved with trading bonds in the secondary market.
▪Efficiently described common trading violations to clients such as: freeride violations, good-faith violations, and liquidation violations. From November 2010 to February 2013 (2 years 4 months) Englewood, COFinancial Analyst Intern @ From May 2010 to October 2010 (6 months) Boulder, CO
Master's Degree, Predictive Analytics @ Northwestern UniversityMaster of Science (M.S.), Business Analytics @ University of Colorado Denver From 2013 to 2014 BA, Economics @ University of Colorado at Boulder From 2006 to 2010 Coursera.com Ryan Chase is skilled in: Forecasting, Data Visualization, Predictive Modeling, Data Science, Tableau, SQL, R, Data Analysis, Microsoft Excel, Analysis, Analytics, Predictive Analytics, Stata, Statistical Data..., Econometric Modeling, Statistical Modeling, Marketing Strategy, Econometrics, Competitive Analysis, Unica, Database Marketing, Business Intelligence, Database Queries
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