Kris Erickson Email and Phone Number

Fact Discovery: Tracing data through the front-end application into the back-end database.Feature Engineering: Creating useful metrics to feed into data systems: Ngrams, pre-post, lead/lag fields, one-hot encoding, mean encoding, normalization and z-score scaling.

Customer Research: Created customer quote and purchase history tables with several distance measurements to quantify quoting patterns and a customer's typical weekly variance. Utilized such methods to classify customers into distinct buckets then passed that information downstream to the internal search team to find different usage models for them to explore.Data Science: Using Google BigQuery I constructed classification models to predict various projects the customer / associate is attempting to construct. Created a basic model to predict whether a quote appeared to be ready-to-sell vs not-yet-ready.Feature Engineering: Constructed BigQuery views collecting and aggregating facts at specific levels to feed into BQ.ML models. Data Quality: Continued vigilance and communication to upstream and downstream teams utilizing datasets provided by THD or our team.

QuoteCenter is a Home Depot application selling bulk products and providing custom delivery solutions to Pros.Data Mining: Managed a conversion model from start-to-finish to predict the chance a quote would generate a sale. Created a call campaign for operations to execute to survey our least likely to convert orders which generated potential problems with our present workflow. Built a way to render a logistic regression model directly into SQL. Built market basket analysis to identify commonly purchased pairs, in addition to general-to-specific models that took into account special order and stock items. Performed text mining of customer surveys and keywords on job and products. Data Quality: Identified and managed data quality issues from beginning to end. From detection to root-cause investigation, then created solutions or mitigation plans which were handed off to ETL or IT team to execute. Finally after implementation I continued to monitor and QA the final product. I located, communicated, and estimated potential attributed revenue and enabled the BI team to build and incorporate the solution, resulting reassignment of over a hundred million in missing sales credit.Tableau Expert: Generated numerous reports which automatically email via Tableau server at periodic intervals, on demand, or upon hitting a conditional threshold. Built final presentations via story-points to narrate final analytic findings that could exported into PDF or PPT. Data Exploration: Searched and discovered new datasources for use by the department from various sources such as Teradata and SQL Server. Extracted data from numerous sources including no-sql formats such as XML and JSON and built initial proof-of-concept ETL to extract the initial data.

Data Visualization: Responsible for Tableau Dashboard development. Analysis of multichannel trends and KPIs.Tableau Server: Maintain user rights, publish data extracts, refresh extracts from command line, troubleshoot usage. Data Engineering: Assist in maintaining jobs and procedures in SQL Server that feed the Tableau Dashboards.Data Mining: Built market basket analysis showing commonly attached items or subclasses that were purchased in-store after picking up an online order. This model resulted in a visualization to see additional up-selling possibility.

Data Engineering: Build / Maintain / Troubleshoot the back-end ETL jobs and packages that relate to associate movement.Data Analysis: Hire-In analysis; Compensation research; Impacts of regional unemployment trends on turnover. Safety & Absenteeism research. Built HR KPI metrics with direction from HR staff. Built a simple churn data model showing the relationship between bad reviews and termination rate.Data Visualization: Build reports, dashboards, and visualizations in Tableau to identify trends / outliers / distributions of various HR metrics. Specifically I built a hire-in rate report to identify new associates pay rates that were very high or very low compared to the median wage.

Responsible for the full BI implementation. Constructed SQL Sever jobs to extract transform and load data into a form that was useful for the business. Responsible for sales analysis and constructing dashboards.Also did research on the drivers of plan adoption.

Responsible for various aspects of sales reporting. Reporting to the controller and assisting salespeople at all levels, from territory managers to the VP of Sales. Dashboards, compensation analysis, territory alignment, data verification and anomaly detection, marketing analysis.