Associate Data Scientist Resume Seattle - Hire IT People

SUMMARY

Proven expertise of solving complex business problems, applying data driven operational excellence models.
Business IT professional with experience more than 9 years handling Aerospace Manufacturing and Reliability sectors, and demonstrated application of functional and technical skills in Data Warehousing.

TECHNICAL SKILLS

Machine Learning Regression: Clustering, Bagging, Boosting, Decision trees

Analytics Languages: R (Intermediate), Python (Expert), BI Query

Visualization tools: Tableau, PowerBi, Alteryx, Power pivot, QlikView

Cloud Computing: Azure Administrator & Architect knowledge

Databases: Teradata, Mongo DB, HBase

Programming: C, C#, VB.Net, Power Shell, SQL

Web Technologies: XML, HTML, JavaScript (Basic)

Big Data: HDFS, MapReduce

Operating systems: Windows, Linux

Natural Learning Process: Chabot, Tensor flow, Lexical, Syntactical & Semantic Process

Deep Learning: Neural Networking, ANN, CNN, RNN

Project Management: Project Planner, Gantt Charts, Microsoft Visio, RCA, Fish Borne, MET (Organization Internal tool)

Microsoft: Power point, Access, Word, Excel (Advanced Excel), Power pivot

Apache: Spark, Airflow, Kafka

PROFESSIONAL EXPERIENCE

Associate Data Scientist

Confidential, Seattle

Responsibilities:

Improved product performance at teh minimum by 10% over teh span of past two years, redefining teh methods of reliability calculation by deep dive analysis on global product user’s environment/related issues after attaining teh deep domain understanding, and eliminating teh false quoted issues from teh customer end product performance results.
Tools/Concepts used are Statistics, Confidential, Machine Learning, Natural Learning, Deep Learning, Python, R
Forecasted teh future data trends, and visualized teh delta of Actual Vs Projected data trends for better business understating using Time Series Forecasting (ARIMA, Moving Average)
Reduced a huge work load of almost 70% by web scrapping various websites using Beautifulsoap, HTML.
Used programming languages like Python, and Vba
Built several machine learning algorithms (like Classification, Clustering, Naïve Bayes etc.) to apply teh data mining concepts such as NLP.
This solved so many problems and helped customer gain confidence about wat is displaying in teh reporting tools, this data will be included in teh Customer product performance statistics, hence this deemed to be a major problem to Customer
Built a robust Power bi, Tableau dashboards displaying
Teh performance of teh product over teh span of time
Product delivery details across each country across teh globe
Applied all teh understanding and knowledge related to Cloud computing while migrating teh existing RDMS DB to NoSQL DB to accommodate both structured and unstructured data.
Used database/languages/tools/concepts such as Azure cloud computing, Apache Spark, HIVE, SQL, Power Shell, Teradata, Mongo DB, JavaScript (Introductory), Business understanding via RGD, HDFS, MapReduce, Yarn etc.

Senior Data Analyst

Confidential

Responsibilities:

Interact with active (200+) product consumers, data consumers and resolve their data related problems with an excellent business solving skills
Annotated, verified teh Clustering, Classification machine learning algorithms inputs and outputs.
Create a basic machine learning models such as NB, Regression on product makers data during data management
Built various Power bi (E - R modelling), Tableau and Alteryx dashboards as a customer reporting
Create teh complex SQL queries as per business requirements such as building teh scripts for batch run processes, back-end counting of automated charts metrics etc.,
Perform teh Confidential, Statistic Inferences, training model and fitting model tasks on complex datasets
Assisted IT while creating a MYSQL database (as received database) with different
Proficient in querying HDFS with HIVE

Data Analyst

Confidential

Responsibilities:

Trained as a Data Analyst for 6 months (Jr. Data Analyst)
Built and/or create different sorts of SQL queries either for data extraction or for data inclusion
Engineered in Data warehousing practices such as Data understanding, cleaning, Imputing, Mart, OLAP & OLTP
Acquired proficiency in data analysis techniques such as Exploratory Data Analysis. Used different concepts such as Uni-Variate, Bi-Variate analysis using Python and Excel pivot charts
Familiar with web tools such as XML, HTML, schemas, DTD, XQuery
Built different varieties of Tableau dashboards to demonstrate teh insights to department heads
Automated some manual efforts using programming languages such as VBA, C#. Saved manual efforts of around 50%
Familiar with file encryptions such as PGP encryption, Message courier, Hub span, Outlook encryption and their importance for proprietary data
Consolidate teh business requirements act as a bridge between business and data scientist

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