SUMMARY

• Experienced working with several machine learning algorithms like Linear Regression, Logistic Regression, SVM, k - means Clustering, Decision Tree, Random forest, KNN, Neural Network, Market Basket Analysis, Data Mining, Deep Learning, Time-series Analysis
• Strong foundation in data mining and statistical concepts like Descriptive and inferential statistics, data collection, hypothesis testing, measuring significance, Data distributions, confidence intervals, and probability distributions
• Proven knowledge in data mining, machine learning and deep learning skills such as Computer Vision, Recommender Systems and Natural Language Processing
• Skilled in data gathering, data cleaning, data transformation, model building and model deployment on structured, semi-structured data and unstructured data
• Hands-on experience with IBM cloud and worked on many Watson services like Knowledge studio, Watson Assistant, Watson Studio, IBM Cognos Dashboard, Natural Language Understanding, Machine Learning, Functions and API’s
• Strong experience in architecting real-time streaming applications and batch style large scale distributed computing applications using tools like Spark Streaming, Spark SQL, Kafka, Flume, Map reduce, Hive
• Strong foundation in data mining and statistical concepts like Descriptive and inferential statistics, data collection, hypothesis testing, measuring significance, Data distributions, confidence intervals, and probability distributions
• Experience in building strategies for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS 'big data' technologies
• Proven verbal and written Communication Skills with experience presenting to large and small audiences
• Ability to interact with peers and stakeholders to define and drive product and business impact

TECHNICAL SKILLS

Languages: Python, SQL, R

Cloud: Google Cloud Platform, AWS, IBM cloud

BI tools: Tableau, PowerBi, Advanced Excel.

Databases: SQL Server, MySQL

Big Data: Spark, Scala, Hive, MongoDB

Miscellaneous: GitHub, ScikitLearn, TensorFlow, Pytorch, Matplotlib, Pandas, Keras, XGBoost, CNN, Docker, Splunk

PROFESSIONAL EXPERIENCE

Confidential

Data Scientist

Responsibilities:

• Developed an employee-friendly chat-bot using IBM Watson Assistant and connected it with the SQL server. The chat-bot answers any questions asked for chat-bot by leveraging data from the SQL Server Database. Used Python, It helps in onboarding new employees
• Worked on Preventive Maintenance to predict machine failure. The machine learning model identifies different readings of the machine and predicts its estimated time before crash based on the data it receives from various sensors. The maintenance costs and other future measures can be arranged based on the status of the machine.
• Used Machine Learning to identify credit card thefts. Created data pipelines using Spark structured streaming and loaded the data into hive tables via Kafka. Applied machine learning algorithms from MLlib to identify fraudulent transactions real-time
• Built ML API's using flask to deploy models and integrated with UI to provide real-time predictions to customers. Defined ideal pipeline profile and created pipeline analytics to help identify ipp and used tools to maintain and monitor pipeline health in CRM systems
• Applied Sentiment analysis on product reviews of different brands and found significant features or keywords from the customers' product reviews. Compared to customer reviews with the competitors in new, international markets
• Integrated data science into product development to capture product viability and used data-driven decision-making to measure product progress. Took insights from clients and measured user experience insights in dealing with the changes or updates for the product
• Estimated feature sales of the product by using demand forecasting, taking account of pricing, promotion, seasonality. Used historical data, along with deep neural network models, to create a scalable model. Performed inventory management using time series analysis to estimate the inventory in supply and sent real-time alerts to reduce or increase the production of supply
• Performed price optimization and used dynamic pricing based on quantity and future trends. Used geographical location to divide the population into different price segment and used predictive modeling to anticipate trends early during a crisis
• Built a marketing budget optimization for social media marketing by finding and using the right channels and right audience by targeting customer profiling considering customer loyalty to be a key priority
• Built a cohesive CBM report and replaced multiple reports from 6 disparate vendors. Acquired data from various vendors in CSV's, excel and PDF formats and loaded them into business one drive. Used logic apps to get data from one drive to ADLS and then to azure Synapsis. Built data-pipelines and tableau reports by using data from Azure synapsis.
• Used TensorFlowX for building and deploying a production-ready machine learning model pipelines with data lifecycle management. Ensured scalability, extensibility, consistency & reproducibility, and modularity for the software development
• Produced real streaming pipelines using spark structured streaming to transfer logs from microservices to SQL Server. Converted unstructured log data to structured data and extracted meaningful information from records. Performed different functions like watermarking, stream-stream joins, aggregations, window functions
• Performed CDC from SQL server to data bricks delta lake and merged incoming new data to the SQL server. Also transferred incoming streaming transactions data to delta lake and used spark to perform ad-hoc analysis on the transaction data
• Used data catalog and crawlers which are used by Athena redshift, EMR for serverless jobs in AWS Glue
• Orchestrated, scheduled, monitored using CloudWatch and used python shell for external integration of multiple services in AWS Glue
• Employed different functions like any, all, stopped, timeout, failed, job delay for monitoring the jobs in AWS Glue

Confidential

Tableau Developer

Responsibilities:

• Created metrics and drill-downs for the First line of Défense and Second Line of Defence dashboard to show annual performance indicators, KPI's and metrics performance like employment training, phishing attacks for client and leaders
• Assisted Application Developers to upload the production-ready applications to the Contrast Security tool. Built Tableau reports leveraging data from Contrast Security to identify and act on critical, severe, medium and low-level vulnerabilities in code, licenses, and libraries for the applications
• Built reports from ServiceNow data for team members to track their tickets, requests, and due days remaining. Sent remainders to manager on the open requests past the due date
• Assigned permissions to groups and people as a viewer, editor, interactor or publisher and provided a story and actionable insights to clients