Venuka Chintapally

• Mentored team members to enhance professional development and accountability in workplace.
• Developed loyal and highly satisfied customer base through proactive management of team customer service strategies.
• Supervised day-to-day operations to meet performance, quality and service expectations.
• Cultivated positive rapport with fellow employees to boost company morale and promote employee retention.
• Set aggressive targets for employees to drive company success and strengthen motivation.
• Work on solving team & business challenges and on opportunities to deliver productivity savings through projects and various data & digital initiatives
• Working Various Automation projects using Qlik Sense, Alteryx and Python
• Working on Executive Oversight, Control Room, Monitoring Analysis and Compliance evaluation program project. Calculating and automating the processes of finding all metrics related to ERC(Ethics, Risks & Compliance) using Qlik Sense
• Developed XGboost model using python to find statistically significant factors in P3 tool requests getting approval before or after start date of actual P3 requests.
• Developed a basic Chatbots using Rasa python for Ethical Survey.

• Utilized advanced querying, visualization and analytics tools to analyze and process complex data sets.
• Worked with stakeholders to develop quarterly roadmaps based on impact, effort and test coordinations.
• Coached and mentored junior data scientists on Python and data mining techniques.
• Extracted and assessed data from databases to drive improvement of product development and business strategies and processes.

Below are the projects that I worked on

Facial Expression Recognition:

Facial expression recognition system is implemented using Python TensorFlow & Keras, Convolution Neural Network (CNN). Collected facial expression dataset with seven facial expression labels as happy, sad, surprise, fear, anger and neutral is used in this project. The system achieved 83% accuracy on testing dataset.

Laps

Predictive Maintenance:

Organizations spend approximately 80% of their time reacting to concerns, while they can proactively be prevented. Predictive maintenance works towards making predictions about potential failures / breakdowns and avoiding them accordingly.

We have used XG boost algorithm that gives a generalized result and Predicting with >90% accuracy

Component wise failure in advance

Document Search Engine(POC):

Developed a search engine for documents using python. Based on given search keyword, system will go to drive and parse through all kind of documents and it will give best matched document and its path. We used various packages like textract, textblob, NLP etc.,

Sentiment analysis(POC):

Worked on Sentiment Analysis. Extracted tweets from twitter and performed Sentiment analysis for keywords given by client. Using python NLP, Textblob, etc.,

Chatbot(POC):

Developed a HR Chatbot for internal purpose using python. Bot will answer all our HR related qustions.

Alerting Engine for Pharma Competitor Intelligence Tool:

Developed an alerting engine to send an alert mail when there is a change observed in competitor repositories and integrate it to CI tool using python.

• Created various Excel documents to assist with pulling metrics data and presenting information to stakeholders for concise explanations of best placement for needed resources.
• Produced monthly reports using advanced Excel spreadsheet functions.
• Collaborated with business-unit leaders to identify and prioritize problems.
• Extracted and interpreted data patterns to translate findings into actionable outcomes.
• Used statistical methods to analyze data and generate useful business reports.
• Tested data prediction algorithms based on historical data.
• Utilized data visualization tools to effectively communicate business insights.
• Implemented code solutions to answer analytic questions and test and assess new methods.

Below are the projects that I worked on

Customer_Churn_Modeling:

Our business problem is reducing customer churn by identifying potential churn candidates beforehand and take proactive actions to make them stay with us. We build Logistic regression model using Sklearn with 80% of accuracy.

Order Sales forecasting:

Built forecasting model for daily sales of CallHealth customer orders using time series ARIMA Model. We have considered data for past 3 years to forecast sales.

Online Reputation Management (ORM) and Sentiment Analysis:

The purpose of online reputation management is to interact with users/customers and get feedback and understand their concerns about our services and improve ourselves accordingly. And create balance, counter misleading trends, and make ourselves best foot forward.

Everyday we used to scrape user comments from various social media sites and send alert to Customer Relations team.

Done Sentiment analysis for Customer reviews to show the visuals in MCC screens.

Competitor Analysis:

Get data from competitor sites to compare our product prices with competitors.

RFM analysis for Customer Segmentation:

Recency, Frequency and Monetary values are key customer characters. These metrics are very important indicators of customer's behavior because frequency and monetory value effects a customer lifetime value and recency affects retention, a measure of engagement.

Interesting segments identified as Champions, Potential Loyalists, New customers, At Risk Customers, Can’t Lose them.

• Drew conclusions and made predictions based on data summaries or statistical analysis.
• Used Minitab and R to prepare tables and graphs to present clinical data or results.
• Responsible for Statistical Process Monitoring for various parameters in Production using different control charts such as IMR-chart, Individual-chart and P-chart.
• Responsible for fitting and comparing both linear and Non-Linear models.
• Responsible for providing Statistical support to Manufacturing Sciences and QC departments.
• Logistic Regression and Parallel line Assay.
• Experience in working on different Statistical Software such as Minitab, SPSS and SAS.

• To support the delivery of quantitative analysis to the IGDF and Development Franchises.
• Assist the programmer/analyst in the planning, execution and close-out deliverables of Clinical Programs/Trials.
• Support the collation and integration of information packages for decision making.
• Pulling the data from different databases using sql.
• Used Regression analysis, logistic regression, Chi-square test, Cross tabulations, Prediction Analysis, Generalized Linear models, Methods of transforming variables to Achieve Linearity etc.,
• Experience in working on different Statistical Software such as Minitab, SPSS and SAS.
• Proc import is to extract data from different sources.
• Generated Tables, Listings and graphs.
• Proc correl to find the correlation between the variables. Proc reg and Proc GLM to do Regression analysis. Proc logistic to do Logistic Regression Analysis. Proc freq for Frequency analysis and Chi-square test. Proc crosstab for Cross tabulation.