Deepak Teja Mangipudi

ETG Project :

• ETG helps clients to choose the right candidate to fit in their required position (job) . based on their current title level product can give suggestions for candidates to choose the next title levels.
• Worked on title progressions , here will take the set of titles those who moved from one level to another in a tenant level, so we can see how many titles can get more progressions , and finalized the titles which were valid that can move from one title level to next level . this used to help the model to train the product .
• Worked on skill standardization here the skills should be in standardized form that can be understood to model and everyone , will take some set of skills from resumes in a particular tenants ,and this skills were cleaned that we get results from different parsers and highlighted the best standard skills and given to model.
• The data will taken on tenant wise and work on titles and skills part to make the use of the product to find the right job to the candidate in the tenant career pages .
• Worked flexible hours across night , weekend and holiday shifts .

Resume Validation :

• From resumes will extract the data of particulars like personal details (name , address , etc.) , Education , Skills , Experience using a resume parser tool .
• Resumes and data will be taken from database using SQL queries .
• And compare the extraction of data with other parsers and validate which performs better .
• understanding where the tools is struggling to extract the data by checking their resumes and helping developers to stabilize them.

Fitscore validation :

Basically fitscore helps in giving grade to candidate after applying to the job based on their skills , experience , distance , job title matching , this grade helps to clients to choose candidates based on their grade wise .

• used to check in annotation feedback tool in respective client and their job data and validate candidates fits that were already done by model to check if any bugs were disturbing .
• used to validate the data manually sometimes the model might do some small errors and will find the sequence of errors and update that to respective superiors .
• Skilled at working independently and collaboratively in a team environment.

• Can play Data Analysis prospective in the Advanced level MS Excel.
• Verification and validation of data used for trainings and models in advanced methods. Testing the model outputs and its working condition manually.
• On a product level , i had worked on various tenants like Honeywell , Cyient , Roche , Abbott etc .

Annotation in Prodigy Tool :

• Used to take data from database mongoDB and add the data of sentences with meta format using python default code .
• Using linux terminal commands data will host in prodigy machines to respective port numbers.
• while data hosting in linux will set some required labels to annotate the sentences .
• then will open the ports in web and prodigy page will appear and will annotate the sentences to respective labels .

Metrics Analysis :

• Checking in looker dashboard in weekly and daily metrics and graphs of widgets of the product how they are working .
• will compare this week and last week graphs of tenants of different widgets and do health checks where it was lagging .
• will work on tenant level findings of each widgets and UI issues .
• Worked effectively in fast-paced environments.