Category: Analytics

1) What prompted you to make Hyreo.com

The concept of Hyreo took shape in our mind as an outcome of the recruiting challenges we faced on a daily basis. All aspects of recruiting are very human labor intense and predictability of outcome at each stage was quite limited. The amount of time spend in sourcing, validating and assessing candidates was very high and hence pretty expensive. The same challenges existed in companies of all sizes. Hyreo took shape in our mind as a possible solution to address some of the recruiting challenges we saw around. We are trying to leverage smart technology and automation to improve the way candidate sourcing, assessment and engagement is carried out. We also felt that the opportunity was quite large since globally the recruitment model and process is fairly standard with limited or minor changes. Availability of technologies including Open NLP and others also helped us decide on building Hyreo as a potential solution to these recruiting problems.

2) In your two year journey as an entrepreneur with  Hyreo, name some
learnings and some turning points.

A few learnings from our entrepreneurial journey:

1. Customers are the most important factor impacting everything – employees, investors & partners
2. Partner as much as possible than build everything in-house and create ‘win-win’ for all parties
3. Be prepared for rejection, it is unavoidable
4. Hire slow but fire fast
5. Entrepreneur knows more about the product than investor, customer or media
6. Marketing is more important than one might think. Place it early in the lifecycle and use it effectively
7. Create evangelists and supporters of the cause early in the game, but never on equity

3) Specifically which need is Hyreo.com trying to address and solve

Hyreo is disrupting the way companies ‘Discover’ and ‘Engage’ with talent. Hyreo leverages smart technology to automate the process of job information dissemination to prospect candidates, understand their interest level and subject proficiency and keep the candidates engaged and up-to date on the latest status 24/7. Build as a SaaS solution with chatbot technology, the platform is able to integrate with legacy systems or exist as a stand-alone system. Hyreo is built in a modular fashion such that customers can choose the product based on specific needs. By using the platform, companies are able to reduce 50% overall effort in recruiting and 40% overall cost with substantial improvement in candidate experience and hence talent brand.

4) What are some of the other innovations you see in the HR space

All aspect of HR and human capital management areas is getting disrupted with legacy processes being challenged by newer technology including Machine learning and AI based systems. Some of the areas that we see interesting innovation and proved merit include:

1. Employee engagement: Be it answering employee queries or addressing issues of the employees, innovative technology solutions including Chatbots are being deployed
2. Candidate reference checks are being automated to ensure the cycle time and the overall effort is reduced considerably
3. Digital Learning platforms including micro learning platforms
4. Intelligent interviewing platforms

 

5) What are some of the obstacles you see to HR innovations.

The journey has just begun and the initial inertia opposing the change has drastically reduced. There is a lot of exciting new technology in the market now, and it will take time for all stakeholders to evaluate options and adopt best practices. Some of the areas we should look at:

1. HR should be a CEOs function and there should be focus on not just improving process but the mindset should be to invest in success
2. There is a need for re-branding HR as a growth catalyst rather than a growth support function
3. Need more investments in HR Tech space

About Hyreo-

 

Missing Value Imputation and Dealing With Outliers

These are an important part of data pre-processing and these are rarely taught in DONKEY ACADEMY who charge you a lot to give you a certificate that doesn’t give you a job.

So okay after that violence and double talk (from Dire Straits) here is how you deal with outliers

1) Replace outliers or missing values them with mean or median – based on distribution -which you see if age< 20 or age>80 then age=median(age)

2) Replace them by capping upper and lower limits. eg an age distribution of 1-120 for bank customers can be capped like if age<20 then age=20 if age>80 then age=80

3) Use MICE package for Imputation (in R) or pandas-mice for Python (https://lnkd.in/f6Z3jj5) eg if males have median age of 50 and females have median age 0f 45, replace all male age missing values with 50 and all female missing values with 45

4) Use OutlierTest in car package in R This is barely the tip of iceberg in missing value and outliers https://lnkd.in/fus_MiF

#machinelearning hashtag#algorithms hashtag#pythonprogramminglanguage hashtag#analytics hashtag#datascience hashtag#python hashtag#rstats

Is KAGGLE a website only for super human data scientists? NO NO NO

You can be a kaggler very easily-

1) Understand how kernels function especially input file and output submission- The best is to use Notebook method not script method of using code

2) Have basic knowledge of EDA and Data Viz in either R or Python ( if you dont know that EDA means exploratory data analysis you can start learning – from Kaggle KERNELS itself

3) Have basic knowledge of Machine Learning Algorithms (and how to apply ) and how to compare Area under Curve (AUC)

4) Deep Learning is advanced and for Python preferably

5) Practice one hour a day. Kaggle is like a gym for the brain if you do this for a year, see where your career zooms.

And one more thing- cross post your code on Github hashtag#bigdata hashtag#love hashtag#machinelearning hashtag#analytics hashtag#datascience hashtag#deeplearning hashtag#python hashtag#r hashtag#howto hashtag#github hashtag#datamining hashtag#datavisualization

Be a data scientist in 6 months. Learn R or SAS or Python in 6 weeks. Learn Data science by doing one capstone project on one dataset.

Sorry mate, there are no short cuts to success.

Your real data science journey begins AFTER you learn the statistics AFTER you learn the techniques AFTER you learn the tools like R/SAS/Python.

A couple of datasets like Iris / Boston / German Credit / Scraping Tweets wont do it. A few weeks on kaggle wont do it.

You probably need to spend a few more months on Kaggle and a few more months on competitive programming like www.hackerrank.com will bring your data science dreams closer.

Disclaimer-I have interviewed potential data scientists and I have taught on some of these kind of courses. #datascience #python #programming #r #statistics  #datasets

https://www.linkedin.com/feed/update/urn:li:activity:6427435737691582464

https://www.linkedin.com/feed/update/urn:li:activity:6426321920102367232

Internships are the magic that a student to convert to data scientist. Not everyone can solve Kaggle contests while still in engineering or other schools. Not everyone has the money to pay for private institute’s training which basically teach R, Python or SAS but not analytical thinking when confronted with a messy real life dataset.

A project in such training is worth much less than the experience of internships.

Companies who have existing data science teams should also try and give internships to create a  steadier supply of data scientists for their operations besides building a bigger brand in data science recruitment.

Meetups and LinkedIn groups( Facebook groups) are good places to offer internships and students should make attending Meetups as a pseudo proxy part of curriculum with the goal of landing internships in each year of their summer/winter break. #analytics #recruiting #python #r #meetup  #engineers #internships #datascience