RE: FW: 3P Findings

Akshay,

Thanks for writing

Unfortunately , my colleague Mitchelle will be busy attending some program for next 3 days

If ok , I request you to visit our office next Tuesday ( at 10 am ? ), when we can carry out that supervised model , under your guidance ( - since we are not quite clear on how to proceed )

Warm Regards,

Hemen Parekh

+91 98675 50808

3P Consultants \ Strategic Human Resources Consultants

From: Akshay Sehgal [mailto:akshay.s@ipredictt.com]
Sent: 17 May 2017 11:08
To: Hemen Parekh
Cc: Rohit Verma; Mitchelle
Subject: Re: FW: 3P Findings

Hi Hemen,

Do let us know what is the status of the requested data required for the supervised model as discussed above.

Also, I am in Mumbai now and will be available to meet for a detailed discussion/brainstorming. Let me know what day, location suits you. You are welcome to visit our office and meet the team anytime. I can share details accordingly.

Thanks,

Akshay.

Co-Founder, Product Strategy & Research

+91-991-659-4778 | Skype : akshaysehgal2005 | http://ipredictt.com/

On Mon, May 8, 2017 at 3:33 PM, Akshay Sehgal <akshay.s@ipredictt.com> wrote:

Hi Hemen,

Next step will be to understand the 'delta' between the selected and predicted. This is what our team has started working on. However, this is to be expected from an unsupervised model.

As would have been discussed before, there are 2 models that should ensure a good target accuracy (getting selected candidate in top 10). 

1. The first model is the unsupervised model which relies primarily on skillset data. Its objective is to cluster similar candidate profiles based on the skillsets and rate which candidate is closest to their 'center of gravity'. This is the selection stage of the hiring process

2. Next is the rejection process. Here we will need data regarding candidates who were considered for a particular profile and whether or not each candidate was finally selected or not. This model doesn't take into account skill sets since that's done already in the previous model. This part of the model only looks at criteria patterns which cause selection or non-selection of a candidate.

The combination of these 2 models will ensure the manually tested accuracy we need. Let's discuss the data requirement for the second model. 

Thanks,

Akshay.

Co-Founder, Product Strategy & Research

+91-991-659-4778 | Skype : akshaysehgal2005 | http://ipredictt.com/

On Mon, May 8, 2017 at 1:38 PM, Hemen Parekh <hcp@recruitguru.com> wrote:

Rohit ,

What do you suggest as next step ?

Warm Regards,

Hemen Parekh

+91 98675 50808

3P Consultants \ Strategic Human Resources Consultants

From: Mitchelle [mailto:mitchelle@3pconsultants.co.in]
Sent: 08 May 2017 12:47
To: Hemen Parekh
Cc: Rohit Verma; akshay.s@ipredictt.com; Nirmit Parekh; Manoj Pandey; Prashant Sharma
Subject: 3P Findings

Dear Sir,

Our findings are listed below :

In both cases the Candidate who was eventually hired by 3P had an Employability score which was less than 70% in the iPredict ranking.

In the GM HR Assignment only 1 of 3P candidate recommended to client figure in the Top 10 iPredict ranking basis Employability score

In the ED SFI Assignment only 2 of 3P candidate recommended to client figure In the Top 10 iPredict ranking basis Employability score

Position	3P RATING			iPredict Employability Score
General Manager Human Resources	Candidates selected for interview	Candidates made offers	Candidates Hired
	Sushil Dekate			100
	Satish Singh	Satish Singh	Satish Singh	69.52
	Akhouri Prasad			64.27

Position	3P RATING			iPredict Employability Score
Executive Director – Foundation Sustainable Agriculture	Candidates selected for interview	Candidates made offers	Candidate Hired
	C.P.Mohan			70.84
	Basant Nayak			65.3
	Sannadi Baskar Reddy	Sannadi Baskar Reddy	Sannadi Baskar Reddy	54.91
	Charudutta Panigrahi			54.15

Do suggest next steps.

Warm Regards,

Mitchelle

+91 98675 50810

3P Consultants \ Strategic Human Resurces Consultants

 From: Rohit Verma [mailto:rohit.v@ipredictt.com]
Sent: 04 May 2017 18:12
To: Mitchelle
Cc: Hemen Parekh; Nirmit Parekh; Prashant Sharma
Subject: Re: Assignment 2- Executive Director Foundation for Sustainable Agriculture

Hi Mitchelle,

We have created a 3P Consultant account for you on the platform.

Please login to enterprise.careerletics.com

UserId: mitchelle@3pconsultants.co.in

Pwd:    3p@789

Following are the advised steps to experience the features –

1.       Review the JD (You can edit or add information  - like Skill sets)

2.       Review the PARSED resumes (You can edit or add information)

3.       Open the dash to see the trends

4.       Download Excel sheet for quick reference (attached)

5.       Review the top listed resumes to decide on next steps

6.       Provide us feedback

We currently ran the parser to collect Resume information and also ran various algorithms to get results for Employability score., Probability of Decline .

Please study the accuracy and let us know patterns you see.

Also share details of selected candidates you have originally have for this role. The funnel should be

1.       Candidates selected for interview

2.       Candidates made offers

3.       Candidates hired

Further, Our Data Science team will work on this to improve results which we will discuss as we progress.

Should you have any issue or need clarity, please call me

Regards,

Rohit

From: Mitchelle <mitchelle@3pconsultants.co.in>
Date: Wednesday, 3 May 2017 at 12:36 PM
To: <rohit.v@ipredictt.com>
Cc: Hemen Parekh <hcp@recruitguru.com>, Nirmit Parekh <nirmit@3pconsultants.co.in>, Prashant Sharma <prashant@3pconsultants.co.in>
Subject: RE: Assignment 2- Executive Director Foundation for Sustainable Agriculture

Dear Rohit,

Many thanks for your response.

As agreed ; we shared with you two descriptive JD’s along with evaluation criteria at our end on the basis of which candidates were shortlisted and resumes (50 batch size/assignment).

Basis these documents shared; we expected the iPredict software to accurately pars these resumes and derive an accurate shortlisting with an employability score.

The objective of this exercise was to then match the candidate shortlist derived out of the iPredict automated algorithm along with an Employability score vis a vis the manual shortlisting done at our end.

This would help us correlate the effectiveness and shortlisting accuracy along with the employability score of the algorithm vis vis manual shortlisting.

We would thus request you to share with us the same for each of the two Assignment details shared with you.

Do also indicate a timeline for us to receive this.

Warm Regards,

Mitchelle

+91 98675 50810

3P Consultants \ Strategic Human Resurces Consultants

From: Rohit Verma [mailto:rohit.v@ipredictt.com]
Sent: Wednesday, May 3, 2017 10:55 AM
To: Mitchelle <mitchelle@3pconsultants.co.in>
Cc: Hemen Parekh <hcp@recruitguru.com>; Nirmit Parekh <nirmit@3pconsultants.co.in>; Prashant Sharma <prashant@3pconsultants.co.in>; Manoj Pandey <contact@3pconsultants.co.in>; Akshay Sehgal <akshay.s@ipredictt.com>
Subject: Re: Assignment 2- Executive Director Foundation for Sustainable Agriculture

Thanks, all for the time and patient hearing.

Hope some of the discussion points intrigued you and I hope we work together in the future to create value in not only recruitment space but also in areas of descriptive dashboards, performance, attrition analytics for companies who you have built relationships with.

As email ID is the key resume identifier, we are tweaking our processes to include this change. Will share the results once done.

My responses in line

Accurate Parsing based on which Accuracy in shortlisting at our end with minimal manual intervention / subjectivity – Currently available

Employability score – Available , needs data for further machine training.

Decline Probability – Available , needs data for further machine training.

How would these algorithms assess Cultural fit – Can be done on case to case basis by accessing the social media data (Facebook, Twitter, Linkedin etc) – refer to the attachment. Can be done on case to case basis

What additional trends / analytics can you predict based on the data input – This depends on the questions you have. For instance, if you are interested in looking at next job trends, can be understood by social media tracking.

Also we would like to see the algorithm extrapolate and show us a comparative table /ranking in terms of relevance of candidate profiles shared to the Data points / Job criteria – Will be mailed

We have intentionally not shared the candidate contact details – as we do not want to activate the chatbot tool at this stage to maintain confidentiality – Sure, but Chatbot only works if manually activated for each of resumes. Its not automated.

Any accurate inferences on ability to predict attrition levels / and when will which position come up for hiring (replacement / new), what kind of hiring trends / industry / function wise that are seeing traction etc.,- Probability of decline is an indicator to that respect/

Regards

Rohit

From: Mitchelle <mitchelle@3pconsultants.co.in>
Date: Tuesday, 2 May 2017 at 4:33 PM
To: <rohit.v@ipredictt.com>
Cc: Hemen Parekh <hcp@recruitguru.com>, Nirmit Parekh <nirmit@3pconsultants.co.in>, Prashant Sharma <prashant@3pconsultants.co.in>, Manoj Pandey <contact@3pconsultants.co.in>
Subject: Assignment 2- Executive Director Foundation for Sustainable Agriculture

Dear Rohit,

Many thanks for the interactive session this morning.

As agreed, enclosed please find the documents for your reference :

Position Specification –Executive Director for the Foundation for Sustainable Agriculture

Evaluation Criteria – Data Points comprising Personal and Technical attributes which formed the basis of our evaluation/elimination criteria

Candidate Profiles sourced for this position

We would be keen to view the following based on the data shared :

1.       Accurate Parsing based on which Accuracy in shortlisting at our end with minimal manual intervention / subjectivity

2.       Employability score

3.       Decline Probability

4.       How would these algorithms assess Cultural fit

5.       What additional trends / analytics can you predict based on the data input

6.       Also we would like to see the algorithm extrapolate and show us a comparative table /ranking in terms of relevance of candidate profiles shared to the Data points / Job criteria

7.       We have intentionally not shared the candidate contact details – as we do not want to activate the chatbot tool at this stage to maintain confidentiality

8.       Any accurate inferences on ability to predict attrition levels / and when will which position come up for hiring (replacement / new), what kind of hiring trends / industry / function wise that are seeing traction etc.,

Do feel free to speak to me in case of any additional inputs required.

Warm Regards,

Mitchelle

+91 98675 50810

3P Consultants \ Strategic Human Resurces Consultants