Bheemeswararao Madugula

• Design, create, test and implement complex machine learning and deep learning models and algorithms that drive analytical solutions throughout organization.
• Work with business managers to frame problem, both mathematically and within business context. Perform exploratory data analysis to gain insights about problem.
• Experienced in proposing ROI based solutions to business driven by AI/ML.
• Leverage variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks to achieve business outcomes
• Leverage advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications to improve model recommendations
• Conduct advanced statistical and other analysis to provide actionable insights, identify trends, and measure performance leveraging TensorFlow.
• Use predictive modeling to increase and optimize customer
  experiences, revenue generation, ad targeting and other business outcomes.
• Identify and develop long term data science processes, frameworks, tools, and standards.
• Comfortable working in programming languages like SQL, PL/SQL and Python, as well as with frameworks like Azure ML Ops and Azure Machine Learning.
• Experience designing solutions in distributed teams using Version Control tools like GIT.
• Curate and connect external data sets for broad enterprise-wide analytic usage Collaborate with internal stakeholders to understand business challenges and develop analytical solutions to optimize business processes & monitor outcomes.
• Have passion to advocate and educate on the value and importance of data driven decision making & analytical methods.
• Mentor and guide data science team members as well as developers and QA engineers working on products.
• Having good understanding on GEP Smart Cloud Platform and MuleSoft, currently contributing in GPDW project to build Data Warehouse which is utilizing data from GEP and expecting AI/ML area to be involved to build decision models to make procurement process as more reliable.

Projects:

1. The following e-Portfolio is having multiple projects which are completed from MIT IDSS.

https://eportfolio.greatlearning.in/bheemesh

2. Space Utilization Prediction

Goal: To predict the space availability in office building.

Description: In office building the space can be utilized differently across in an year, and the model can predict the space availability in a specific time period to estimate the flexibility of the space capacity.

Skills: Trifacta wrangler, Python, Pandas, scikit-learn, Phoenix End End End Model: Time series model

3. TAT (Turn Around Time)

Goal: To predict the sizing of resources in specific period

Description: The functional team should take care of the daily support tickets, usually the team size and the ticket flow will vary in different time periods in an year. So the model can predict the team sizing on a particular time period so that the team sizing can be maintained dynamically.

Skills: Trifacta wrangler, Python, Pandas, scikit-learn, Phoenix

End Model: Time series model

4. FACTIVA

Goal: To predict the category of the news journal

Description: In banking there are different kinds of news journals will be published daily and these can be categorized in different subjects, usually we get these details in emails with different descriptions. So the model can predict the category of the description of the news journal. And we use topic modeling which comes under NLP.

Skills: Python, Pandas, NLP, Countvectorizer/Tfidf vectorizer, Phoenix End Model: Topic Modelling ( Latent Dirichlet Allocation)

5. HR Chatbot

Goal: To crate a chat assistant to retrieve HR related information Description: There are many FAQ's are maintained in HR portal, by searching different topics in the portal to get the corresponding details is little difficult and right response Should be filtered out manually. So we build a chat assistant to interact and retrieve the right response and also the further action to be taken to complete the requirement of the user.

Skills: RASA, Python, Pandas, Word2vec, HTML, CSS, JavaScript, Phoenix

End Model: HR Chatbot

6. GPDW (Global Procurement Data Warehouse)

Goal: To build a Data Warehouse by using the GEP Smart Cloud data.

Description: There are multiple up stream sources are being used in Procurement applications and we planned to migrate one of the source Ariba (current application) to GEP Smart and use the GEP as the up stream data (New Process) and build the Data Warehouse to manage procurement process globally.

Skills: Oracle DB, SQL, PL/SQL, Informatica 10.4, UNIX, GEP Smart,MuleSoft.

Domain: Global Procurement

End Goal: GPDW Data Warehouse

• SnapLogic tool Development and testing, SnapLogic is a commercial software company that provides Integration Platform as a Service (iPaaS) tools for connecting Cloud data sources, SaaS applications and on-premises business software applications.
• SnapLogic’s low-code/no-code platform makes it easy for data teams to quickly hydrate data lakes, create data pipelines, and empower business teams with insights needed for business decisions that improve customer experiences.

• Worked in Amazon Client location as an ETL Developer.
• Worked on source system analysis and data extraction from various sources like Flat files, ORACLE 8i/9i/10g/11g, SQL Server.
• Experience in writing complex sub queries, Views, Functions, procedures, Triggers and Packages using different features in PL/SQL.
• Extensively worked on Teradata Client Utilities like BTEQ, FASTLOAD, MULTILOAD, TPUMP, FASTEXPORT, QUERYMAN and protection concepts like Fallback, Journals.
• Worked on various Informatica Transformations and build complex Mappings, Mapplets, tasks and workflows.

• Interacting with business analysts to understand user requirements, functional specifications and system specifications to Delivering high quality Code.
• Developed complex queries, functions, store procedures, triggers and packages using SQL and PL/SQL.
• Improved performance of SQL queries by utilizing best optimization techniques.

• Interacting with business analysts to understand user requirements, functional specifications and system specifications to Delivering high quality Code.
• Developed complex queries, functions, store procedures, triggers and packages using SQL and PL/SQL.
• Improved performance of SQL queries by utilizing best optimization techniques.