Aspire Analytics

Machine Learning Engineer - Consultant 

At Aspire Analytics, I currently serve as a consultant data scientist,  focusing on ecommerce, sales, marketing, and sensor data.

Data Scientist - Consultant

YouTube

Creating educational content about popular data science tools, data science life, etc.

YouTube Content Creator

The Home Depot

Led a team of 3 data scientists to build, create, and test models used in recommendation system for the HD website. Responsibilities included project management of multiple ML projects, deployment of models, and managing development workflows.

Other Projects and Responsibilities
o Mentored 4 junior data scientists and produced high-quality work;
o Identified opportunities based on regional and temporal data that provided actionable internal insights;
o Designed and implemented a hierarchical framework to better fill the data gap as well as validate data reliability.

Senior Data Scientist - Search and Recommendation Systems 

Brinks Home Security

At Brinks Home, I wore multiple hats: data modeler, data analyst, and data governance analyst. I worked closely with Sales, Marketing, and Finance, to create data and machine learning models from user requirements, documentation, improve data quality, and provide support for data engineering efforts.

This included user acceptance testing of data models and quality testing for data pipelines and ML models

Data Governance Analyst - Analytics Center of Excellence 

edison.ai

As a member of a growing start up in ecommerce space, I was responsible for:

• Implemented machine learning model (k-Means, k-modes, and k-prototypes ) for clustering of customer data to determine potential leads 
• Created Marketing Mix Model using machine learning models to determine the number of prospective customers from marketing channels.
• Managed and configured ETL pipelines using Azure Databricks and Data Factory
• Utilized Microsoft Azure ML studio and Alteryx to create automated Machine Learning workflows. 

𝐓𝐨𝐨𝐥𝐬:
Python, PySpark,  SQL, Tableau (Server/Visualization), Azure (Databricks, Data Factory, MLStudio, and Purview), SSIS, SSMS, Alteryx

Data Scientist - Marketing and Ecommerce 

Provide business intelligence support and analysis for clients in Asia Pacific, European, and North American regions. Main focuses included digital marketing, e-commerce, and sales analytics. 

𝐓𝐨𝐨𝐥𝐬:
Python, Go, PySpark, SQL, Tableau (Server/Visualization), Azure (Databricks, Data Factory, and Purview), AWS, Alteryx, GitHub

 Data Analyst Consultant

Meison

*Toula Corporation acquired by Meison*

Senior data analyst at a fashion company. I processed, analyzed, and ran statistical analysis on marketing, ecommerce, and sales data for three luxury brands. Retailers who sold our product included Nieman Marcus, Dillard's, Macy's, and Nordstrom. 

● Performed statistical analysis on marketing, ecommerce, and sales data. 
● 10% increase in purchases by using market basket analysis to recommend products commonly bought together.
● Marketing Mix Modeling for email, social media, and print media campaigns

Senior Data Analyst 

Ruffalo Noel Levitz

As a Senior Digital Analyst, I led a team of 3 data analysts to ingest data and analyze from multiple digital marketing, email, and web analytics.

Senior Digital Analyst

*Converge acquired by Ruffalo Noel Levitz in April 2019*

Responsible for managing higher education clients' strategic branding and marketing objectives using digital, print, and phone marketing campaigns. Led a team of 4 analysts to achieve execution of digital strategies. Managed a marketing budget averaging $200,000 between 8 client accounts simultaneously. Clients included John Hopkins, University of Southern California, and Columbia University. 

𝐓𝐨𝐨𝐥𝐬:
Excel, Google Analytics, Supermetrics, Google BigQuery, SQL

Digital Strategist 

Carrefour

Lead Sales And Marketing Coordinator

Drake University

Analytics Manager - International Admissions and Marketing 

𝐃𝐢𝐬𝐜𝐥𝐚𝐢𝐦𝐞𝐫:  𝘖𝘱𝘪𝘯𝘪𝘰𝘯𝘴 𝘦𝘹𝘱𝘳𝘦𝘴𝘴𝘦𝘥 𝘢𝘳𝘦 𝘴𝘰𝘭𝘦𝘭𝘺 𝘮𝘺 𝘰𝘸𝘯 𝘢𝘯𝘥 𝘥𝘰 𝘯𝘰𝘵 𝘦𝘹𝘱𝘳𝘦𝘴𝘴 𝘵𝘩𝘦 𝘷𝘪𝘦𝘸𝘴 𝘰𝘳 𝘰𝘱𝘪𝘯𝘪𝘰𝘯𝘴 𝘰𝘧 𝘮𝘺 𝘦𝘮𝘱𝘭𝘰𝘺𝘦𝘳.

Spent 10 years wandering though data, before realizing that I love math, statistics, and programming. Couldn’t make up my mind which to pick. So I decided to pursue data science. And do all of them. It was worth it. 

I am a data scientist who lives to experiment and learn. I am constantly seeking out knowledge and being excited by the challenge of learning something new in the Data Science space. MLOps, Machine Learning, data quality and data governance are my passions. I like tinkering in SQL, Python, and other languages to discover new things.

I have over 4 years of experience in the trenches doing data science and analytics. During that time, I’ve worn many hats from data analyst to data governance analyst to data scientist.

Mentoring people making the transition into data science field drives me. Whether you are a new student starting out, or making a career switch (I did it 3x), I’m interested in learning your story. I’ve learned from helping and mentoring a few of you, and hope to keep doing so. 

In my spare time, I’m a reader, lover of languages, and mathematics.

Ever questioned the importance of tech and models in Machine Learning (ML)? Here's another persepective.

Many times, the spotlight is on the tech and models. We chase after the latest algorithms, hoping they'll be the magic wand. 

The true magic lies not in the technology, but in the process. And how well you can communicate the value and outcomes they bring. This gets more important as you become a senior or staff data scientist. 

So how can you refocus? Here are three simple steps:

1. 𝐈𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐭𝐡𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 Start with the end in mind. What challenge are you trying to solve? Define it clearly. Predictive analytics projects often fail because a model was priortized over solving a business need. Scoping matters.

2. 𝐃𝐞𝐬𝐢𝐠𝐧 𝐭𝐡𝐞 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧. Think of non-ML solutions to a problem. Then think about ML solutions to a problem. Start with a simple model, then build. Remember, the model is a means to an end, not the end itself.

3. 𝐌𝐞𝐚𝐬𝐮𝐫𝐞 𝐭𝐡𝐞 𝐈𝐦𝐩𝐚𝐜𝐭  Assess the impact of your solution. Did it solve the problem effectively? Did it add value? What you think is effective or adds value may not what the business thinks adds values. Have frequent check ins. 

The magic of ML isn't just in the technology itself. It's in the value you can clearly demonstrate, the problems it can solve. 

Keep your focus on the true goal: delivering value and good processes. 

What's your experience been like? Let me know in the comments. 


#datascience #datalife360 #machinelearning #ml #ai

Matthew Blasa

Culture shapes your machine learning models. I'm not talking about company culture, but national culture. As AI continues to weave itself into the fabric of our daily lives? 

It's really important that we get how cultural differences come into play when we're dealing with data and machine learning.

So, how does culture play into ML models? Well, there are three key areas:

1️⃣ How we collect data
2️⃣ How we interpret that data
3️⃣ How we go about developing the ML models 

Culture becomes a major factor if you are doing AI and ML projects with diverse groups. Being able to factor the culture builds great models that adapt well to cultural realities. 

Check out the first part of the series on Substack: 
https://lnkd.in/gtexTv8e

#datacience #machinelearning #ml #ai

Open article: Is Your Machine Learning Model Lost in Translation? [Part 1] by datalife360.substack.com

https://datalife360.substack.com/p/is-your-machine-learning-model-lost?sd=pf

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