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Data Analyst Resume

Bradenton, FL

SUMMARY

  • Skillful Data Scientist with over 10 years of experience in Information Technology / Python / R/Looker/Tableau and dedicated 4.5 years of experience into data science, machine learning, natural language processing (NLP), & predictive modelling.
  • Proficient in structured / unstructured data, data modeling, data mining, and data profiling.
  • Successfully completed 5 data science projects
  • Expert in data cleaning, features scaling, features engineering using Pandas and NumPy packages in python
  • Extensive experience in developing different statistical machine learning, data mining solutions to various business problems and generated data visualizations using R / Python
  • Proficient in machine learning algorithms and statistical modeling like decision trees, text analytics, natural language processing (NLP), supervised / unsupervised, regression models, social network analysis, neural networks, deep learning, SVM, and clustering using scikit - learn package in Python / R
  • Well-versed in Predictive Modeling with SAS, R, and Python
  • Expertise in MLlib, Spark's Machine learning library to build and evaluate different models
  • Automated Data Import Script using shell scripting, PHP, MySQL and regular expressions.
  • Skillful in Natural Language Processing (NLP) for speech recognition and used word to vec to understand the association of the words
  • Worked on Jupyter notebook to visualize stores using maps

TECHNICAL SKILLS

  • Data Science
  • Python
  • R
  • MySQL
  • Web analytics
  • Clustering & Segmentation
  • SQL
  • Python
  • RStudio
  • PostgreSQL
  • DataBricks
  • PySpark
  • GIS
  • PostGIS
  • Flask
  • Databricks
  • Integration Services (SSIS)
  • Oracle 9i OLAP
  • MS Office Web Components (OWC11)
  • JDBC
  • HTML5
  • DHTML
  • XML
  • CSS3
  • Web Services
  • WSDL
  • Erwin R 9.6 9.5 9.1 8. x
  • Rational Rose
  • MS Visio
  • Spark peg
  • Hive
  • HDFS
  • Map Reduce
  • Pig
  • Kafka
  • SQL
  • Hive
  • Impala
  • Pig
  • Spark SQL
  • SQLServer
  • MySQL
  • MS Access
  • HDFS
  • HBase
  • Teradata
  • Netezza
  • MongoDB
  • Cassandra
  • SAP HANA
  • MS Office (Word/Excel/Power Point/Visio)
  • Tableau
  • Rstudio Markdown
  • Business Intelligence
  • SSRS
  • SVM
  • GitHub
  • Tableau
  • Azure Data Warehouse
  • Windows
  • Linux
  • Unix

PROFESSIONAL EXPERIENCE

Confidential

Data Analyst

Responsibilities:

  • Analyzed data to tell astory (what happened, why it happened, what caused the numbers) and Recommended (what should the leadership do to improve)
  • Performed data analysis to support and implement business processes and decisions
  • Perform analytical deep dives to analyze problems and opportunities, identify the hypothesis and design & execute experiments and provide recommendations into key performance metrics and goals
  • Designed, created, and maintained dashboards for KPI monitoring and identifying trends & discrepancies
  • Developed a deep understanding of customer journey phases and key business metrics. Understand how and why customers engage with your product.
  • Enhanced UX by using segment debugger and analyzing the API calls
  • Utilized RedShift, SQL, Looker and tableau tool on a daily basis to provide analytics support across the business, including sales, product, marketing and executive teams
  • Created efficient PDTs and improve run time of the looker dashboards
  • Evaluated success of product initiatives by setting success criteria and analyzing results
  • Provided analytics perspective to aid in product planning
  • Good understanding of data architecture process
  • Troubleshoot within backend developer tool to resolve BI issues
  • Defined milestones, deliverables and communicated project scope, requirements
  • Acted as liaison between clients and technical teams
  • Developed communication plan and improved team collaboration between onsite and offshore teams
  • Worked on ad hoc requests from internal clients.
  • Documented functional and business requirements in collaboration with the product development team
  • Worked with project or functional leads in developing use cases/ scenarios, flow diagrams and performing workflow analysis
  • Managed cross-functional projects by providing project leadership and daily management throughout the project from inception to delivery
  • Managed the feature development and defect backlog on a daily basis to ensure that priorities adhere to the strategic direction outlined by the Product Manager
  • Work closely with Product Management group to provide data solutions to issues impacting customers
  • Assisted with building training material content for new functionality developed for Collections Management
  • Translated complex concepts into implications for the business via excellent communication skills, both verbal and written
  • Extract meaningful insights through analyzing large, complex, multi-dimensional customer behavior data sets
  • Identify key trends and build executive-facing dashboards to track the progress of acquisition, monetization, and engagement trends.
  • Inform future experimentation design and roadmaps by performing exploratory analysis to understand user engagement behavior and derive insights

Confidential, Bradenton, FL

Data Scientist

Responsibilities:

  • Performed data cleaning and feature selection using MLlib package in PySpark on Databricks
  • Performed data cleaning, features scaling, features engineering using pandas and NumPy packages in Python
  • Used NLP for sentiment analysis for the insurance and medical records
  • Worked on Jupyter notebook to visualize stores using maps
  • Built Factor Analysis and Cluster Analysis models using Python SciPy to classify customers into different target groups
  • Used clustering techniques like DBSCAN, K-means, and Hierarchical clustering for customer profiling to design discount plans according to their behavior pattern in R and Python
  • Performed spatial analysis using QGIS and manipulating the geometry types to visualize the data using shape files
  • Used GIS (geographic information system) framework for gathering, managing, and analyzing data
  • Generated visual maps using map projections in QGIS and used Confidential analytics
  • Performed data preparation, loading, and management in an QGIS PostgreSQL server environment
  • Developed good understanding of SRID, CRS, SRS, Vector Geometry, and Geodata (GeoJSON / WKB / WKT formats)
  • Automated manual MS Excel tasks using macros which helped boost productivity
  • Used VLOOKUP on raw excel file and other solver tools for better efficiency
  • Created / designed reports that will use gathered metrics to infer & draw logical conclusions of past and future behavior
  • Used containers like Docker for version control and seamless accessibility for clients
  • Built Docker image to preprocess data and deploy model as an API
  • Used R packages like BSTS (Bayesian structural time series), Boom, BoomSpikeSlab to infer Causal Impact and compare control group vs test group in a post period intervention in RStudio
  • Generated graphs and reports using ggplot package in RStudio for analytical models
  • Used RStudio Markdown for reporting and storytelling
  • Worked on application of various machine learning algorithms and statistical modeling like decision trees, text analytics, natural language processing (NLP), supervised / unsupervised, regression models, social network analysis, neural networks, deep learning, SVM, clustering to identify volume using scikit-learn package in Python / R
  • Used Principal Component Analysis in feature engineering to analyze high dimensional data
  • Used NMF algorithm to produce latent features of an ACS dataset and plot it on a map to understand similarities between each state to further use it in optimized targeting
  • Used Agile methodology and Scrum process for project developing

Confidential

Data Scientist - I

Responsibilities:

  • Worked in a team of Data Scientists/ML Engineers to build and deliver multiple machine learning applications & data products with most of prototypes done in Python
  • Developed multiple recommender systems that helps customer build customized vehicles, recommend warranty packages, parts, services, and used Clickstream data analysis
  • Used Natural Language Processing (NLP) for speech recognition and used word to vec to understand the association of the words
  • Used Jupyter notebook for Python coding
  • Defined end-user experience and benefits individual recommender system
  • Evangelized needs and benefits of recommender system to executive managements
  • Presented business insights about the user behavior and product behavior
  • Used macros for raw excel data files and made it useable for analytics
  • Worked with team to develop state of the art recommender system using customer history /product history and used Kubernetes for scaling & development
  • Evaluated multiple models based on affinity analysis, Deep & Wide Neural Network, ALS Matrix Factorization Model, Hybrid Collaborative Filtering with user based, and item-based models
  • Formulated A/B testing metric and designed automated scheduled workflow to perform continuous A/B testing
  • Deployed recommender models through web APIs to allow downstream applications for each new version of recommender system to easily consume recommendations
  • Reduced custom built configuration time by 30% using the model
  • Increased product (parts, warranty package) sales by 12% using the model
  • Developed & deployed AI model that performed NLU (natural language understanding) on customer complaint and used vehicle configuration to predict repair hours, failed component/part, and rank procedures
  • Performed data manipulation, data visualization, and feature engineering
  • Performed feature engineering on text using word2vec model andhandled imbalanced class problem
  • Built multiple ML models like Fully Connected Multi-Layer Neural Networks, CNN, ANN, RNN-GRU, Memory Network, Conv1D, and GLM
  • Used libraries like TensorFlow and PyTorch(Rtorch with R)
  • Evaluated model on multiple evaluation metrics like Top-K Mean Average Precision, KAPPA score, Precision,and Recall
  • Predicted from more than 4.8K unique vehicle components/parts using model and achieved 82% accuracy on test set
  • Deployed the model successfully on multiple downstream applications used by customer, dealership, technicians, and fleet managers
  • Reduced average vehicle issue diagnosis time by 40% using model

Confidential

Junior Data Scientist

Responsibilities:

  • Built Dynamic Forecasting Models across multiple dimensions served in real-time & batch process e.g. Weekly Vehicle Retail Forecast at models, categories, region & dealers, monthly/weekly warranty &parts forecast
  • Identified the use case and laid out quantifiable benefits & clearly defined risks
  • Developed / implemented R and Shiny application which showcases machine learning for business forecasting
  • Performed validation on machine learning output from R
  • Coordinated with Senior Director and Executives to present analytics workflow
  • Co-led the team to identify machine learning approaches that could deliver quick results
  • Evaluated merits of individual ML approaches and established evaluation metrics
  • Built forecasting models like Forecast Hybrid, STL Arimax, RNN-LSTM,RNN-GRU, hierarchical &grouped time series, dynamic regression, vector autoregressions
  • Formulated end-to-end solution and monitored team’s progress
  • Delivered successful forecasting model with 85% accuracy for 12 month’s forecast horizons

Confidential

Ad-Ops Engineer

Responsibilities:

  • Performed trafficking and delivery of all advertising related projects
  • Managed digital advertising campaigns including Ad trafficking, packing, optimization, reporting, and monitor campaign progress
  • Handled various tracking problems, ad tag implementation effectively
  • Developed solutions / documented internal procedures, policies, and tutorials regarding ad tracking & targeting for DoubleClick, AdMob, AdSense / AdWords
  • Worked with Product Management, Marketing, Engineering, and other teams to inform, test, and implement product and operational improvements
  • Optimized campaign performance by recommending adjustments to inventory and creative components of the campaign

Confidential

Data Analyst

Responsibilities:

  • Worked with data governance, data quality, data lineage, and data architect to design various models.
  • Delivered MS Excel tasks using macros
  • Analyzed large and complex datasets to investigate and identify fraud scheme trends and improve efficiency in fraud detection process.
  • Classified fraud accounts to deconstruct emerging patterns.
  • Built and managed reports and dashboards to monitor KPIs.
  • Prepared technical design documents and test cases

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