Call Center Analyst Resume

SUMMARY

Entity relationship diagrams, normalized tables, data extraction, joins, advanced queries, subqueries, DML (Data Manipulation Language), DDL (Data Definition Language), date/time manipulation, group operations
I’ve designed a relational database to process orders and to produce sales related reports for management as shown in my GitHub.
SQL in PL/SQL, functions, stored procedures, packages, cursor, exceptions, triggers, calling a procedure from the internet
Machine learning, deep learning, computer vision, natural language processing, association rule learning, reinforcement learning, TensorFlow, Keras, PyTorch, Theano
ETL, IN/out, preparation, join, transform, predictive tools, SQL, joins
I use the ETL software to analyze large data sets over 2 GBs. What I used to do with Excel and Access, I now do on Alteryx with ease and greater efficiency.
PySpark, MLlib (Linear, Logistic, Decision Trees, Random Forests, K - Means, NLP)
I import SparkSession then create an assembler object using VectorAssembler. I select that object assembler along with the dependent variable then create a train and test data sets using a .7 and .3 split. Then I import the respective machine learning algorithm. After I fit the training model onto the test model then follow up with the results such as checking for the area under the curve.
Ambari, HDFS, YARN, MapReduce, Tez, Pig, Hive, HBase
I enjoy using Pig due to its similarity to SQL’s syntax. I used an IMDb dataset containing data about movies. Through Pig, I sorted which movies had the highest ratings.
Time series, aggregation, level of granularity, filters, time maps, scatterplots, joining data, blending data, table calculations, advanced dashboards, storytelling advanced data preparation
I use Tableau to make visually appealing and easy to understand sheets, dashboards, and stories. An example is when I used a csv file about an imaginary bank operating in the UK. The dashboard was segmented by age, balance in their bank accounts, region, age, job classification, and gender. England showed a high percentage of white-collared workers aged between 30 to 40 years old so if marketing were to be implemented in, this bank should market towards white-collared workers in their mid-30’s.
Brief use case descriptions, use case diagrams, CRUD matrix, activity diagrams, system sequence diagrams, domain model class diagrams, design class diagrams, agile, waterfall, UML, Visio
Our team of six people created a complex IS application called Pop In where organizations can promote their events and customers are able to tap right to like the event or tap left to delete the event, similar to Tinder. A working prototype was created using Justinmind.

PROFESSIONAL EXPERIENCE

Confidential

Call Center Analyst

Responsibilities:

Utilized proprietary databases to obtain information from over 50 departments upon request
Verified data was consistent and valid across software programs to minimize discrepancies between the business and the patient
Operated in a high volume, highly structured call center environment by responding to an average of 100 calls per shift
Upheld HIPAA regulations

Confidential

Data Analyst

Responsibilities:

Executed A/B testing on advertisements using Facebook Ads, Instagram Ads, & MailChimp which resulted in 90 leads for a real estate client
Consulted with businesses on their online strategy such as social media outlets, websites, and email marketing
Negotiated contracts between the business and Confidential Time Digital
Developed websites for businesses within 2 days through HTML 5, CSS 3, Bootstrap 4, & JavaScript
Delivered weekly reports to the stakeholder using Google Analytics, Facebook Ads Manager, and Facebook Pixel
Used Agile methodologies through KPIs to increase targeted audience engagement

Confidential

Data Analyst

Responsibilities:

I’ve completed a case study for a medium sized company of about 300 employees.
The company did not have the resources to track the effectiveness of their campaigns.
I was given the task to look at the data and I answered the following: click-through rate, number of leads, cost per click, cost per lead, customer acquisition cost, customer lifetime value, net promoter score, number of touch points, and customer retention rate (likely to buy again).
I created PivotCharts for further analysis.

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