RE: STANDING STILL ON A TREADMILL

 Mitchelle ,

 

No hurry – especially since this would need a lot of thinking-through ( as to what value-added service , do we plan to deliver to our clients )

 

In this context , my following chats with ChatGPT would help

 

Hcp

 

 

I SAID :

 

 

Hey , ChatGPT :

 

Graphical Profiles are very good for enabling recruiters / employers to quickly grasp the " Standing " of a given candidate, relative to her " Comparable Co-Professionals " .

 

That is possible only if we have a very large database ( of resumes ) of such comparable co-professionals. We should be able to compare, " Apple for Apple " .

 

Unfortunately the graphical profiles which I submitted to you earlier, club together, a " Sales Officer " having 2 years work experience with a " Vice President, Sales " , having 20 years experience, just because both of them belong to " SALES " function ! And the total database of " SALES " function professionals in my database is barely 3000 !

 

Ideally , if the database of " SALES " professional resumes was to be 3 million , I could , possibly have, a SUB-GROUP of " SENIOR SALES PROFESSIONALS " of , say , 30,000 - all of them, being COMPARABLE .

 

I believe , Linkedin has such huge databases of " Comparable Professionals " . Is there any method whereby , I can access these SUB-GROUPS , and generate graphical profiles , which make sense ? How do I go about including a given executive and show her " Relative Standing " among these 30,000 ?

 

ChatGPT said:

 

To build meaningful Graphical Profiles that compare a candidate's "Relative Standing" against comparable co-professionals, the challenge you're facing with your current database is understandable.

 

With only 3,000 resumes, the comparisons between vastly different levels of experience (e.g., a Sales Officer with a Vice President) in the same function don’t yield insightful results. Here’s how you might leverage larger databases (like LinkedIn’s) and improve the relative comparison:

 

Steps to Access and Utilize Large Databases (e.g., LinkedIn):

 

1.LinkedIn Data Access via API:

 

o    LinkedIn has an API (Application Programming Interface) that allows you to query their vast database. Although LinkedIn has restricted access to its full API, there are ways to obtain relevant data for building comparable professional groups, such as via LinkedIn Recruiter or LinkedIn Premium subscriptions.

o     

o    You can extract profiles based on filters such as job titles, experience levels, industries, locations, and skills to form specific sub-groups. This will allow you to isolate professionals with similar seniority, experience, and skills to build valid comparative datasets.

o     

2.Advanced Search Filters:

 

o    On LinkedIn, using Boolean search operators and filters (job titles, years of experience, function, etc.), you can narrow down professionals in a specific domain. For example, you could use filters like:

§  Function: Sales

§  Job title: Director, VP, Executive

§  Years of Experience: 15+

§  Industry: Technology, Consumer Goods, etc.

§   

o    This process helps you create a more comparable cohort of professionals with similar career paths, education, and seniority levels.

 

3.Data Collection Through Web Scraping:

 

o    Although LinkedIn does not allow web scraping directly (due to privacy concerns), there are third-party tools (e.g., PhantomBuster, Octoparse) that can extract public data from profiles (subject to compliance with data privacy laws). This could help you gather anonymized career data to analyze trends and create comparable sub-groups.

o     

o    Ensure compliance with LinkedIn's terms of service and data privacy regulations while pursuing this approach.

 

4.Third-Party Professional Databases:

 

o    Companies like Glassdoor, Indeed, or AngelList may offer access to professional data (sometimes paid services) that allow deeper insights into salary, career progression, and skill comparisons.

o     

o    You could use these sources to supplement your dataset and build sub-groups of comparable professionals.

 

Generating Graphical Profiles:

Once you access and build a more relevant subset of professionals (e.g., 30,000 senior sales professionals), here’s how you can proceed to generate insightful profiles:

1.Segmentation of Data:

 

 

o    Use data clustering techniques (such as K-means clustering) to segment professionals based on various criteria such as years of experience, role level, or salary range. This will ensure that you're comparing a candidate to others at a similar career stage.

o     

2.Generate Comparative Graphical Profiles:

 

o    Use a data visualization tool like Tableau, Power BI, or Google Data Studio to create visual graphs:

o     

§  Experience vs. Tenure comparisons for senior professionals.

§   

§  Salary Percentile based on role and industry.

§   

§  Skill-Level Comparisons where you rank professionals by proficiency in top industry skills.

o     

3.Creating Relative Standing in a Sub-Group:

 

o    Once you have a defined dataset (e.g., 30,000 similar profiles), benchmark the candidate against metrics like:

o     

§  Median tenure at a specific job level.

§   

§  Average salary for a specific job title.

§   

§  Progression rate from Manager to Director or VP levels.

§   

§  Industry-specific key skills and certifications.

o     

Example: Relative Standing in Graphs

If a candidate has 18 years of experience and is currently a VP of Sales:

• Experience Graph: Compare them against the mean and percentile scores of 30,000 VPs with 15–20 years of experience.
•  
• Salary Profile: Show the candidate’s salary against the mean salary and 1-sigma variance in the sub-group, giving insight into whether they are overpaid, underpaid, or in line with industry peers.
•  
• Promotion Rate Graph: Measure how often other professionals in this cohort have been promoted and compare it to the candidate’s history.

 

Key Considerations:

• Data Privacy: Ensure compliance with GDPR and LinkedIn’s terms by anonymizing any sensitive data.
•  
• Accuracy of Sub-Groups: Be meticulous in filtering and defining your sub-groups to avoid biased comparisons (e.g., mixing early-career candidates with senior-level professionals).
•  

By leveraging LinkedIn’s database and other third-party resources, combined with clustering and visualization techniques, you can refine your Graphical Profiles to provide recruiters with a more accurate and meaningful comparison. This would enhance the CANDIDATE BOT concept, allowing it to accurately show where a candidate stands in relation to truly comparable peers in the job market.

 

I said:

Many thanks for your tips on how to access fairly large ( 30K to 100K ) sub-groups of " comparable co-professionals " from Linkedin - of course , in an anonymous aggregation. Now can you help me by telling me what FREE software can I use to generate graphical profiles of these sub-groups which come close to a NORMAL ( Binomial ? ) distribution curves ?

 

ChatGPT said:

To generate graphical profiles resembling normal (or binomial) distribution curves for large sub-groups of comparable co-professionals, here are some free software tools you can use:

1. Google Sheets or Microsoft Excel (Free Online Version)

• Use Cases: Basic data analysis, histogram generation, normal distribution curves.
• Features:
• You can create histograms and scatter plots.
• Use built-in statistical functions to calculate mean, standard deviation, and percentiles.
• Easily plot data and fit a normal distribution curve using formulas.
• How: Input your dataset and generate histograms. For normal distribution, use the formula NORM.DIST for Excel/Sheets.

2. Jupyter Notebooks (Python + Libraries like Matplotlib and Seaborn)

• Use Cases: Advanced data visualization with customization options.
• Features:
• Use Python libraries like Matplotlib, Seaborn, Pandas, and NumPy for generating distribution curves.
• You can plot normal or binomial distributions based on the data and fit the curve to your dataset.
• Perfect for handling larger datasets and providing more detailed insights.
• How: Use matplotlib.pyplot.hist() for histograms and seaborn.distplot() for distribution plots with the option to fit a normal curve.
• Note: Free to use with Python installations (e.g., Anaconda).

3. R + RStudio (with ggplot2)

• Use Cases: Powerful statistical computing and visualization.
• Features:
• RStudio, combined with the ggplot2 library, is excellent for producing detailed statistical graphs, including normal and binomial distributions.
• It offers flexibility to add mean, sigma values, and percentile lines to your graphs.
• How: Use ggplot with geom_histogram() for histograms and stat_function() to overlay the normal distribution curve.
• Note: Free and open-source.

4. Tableau Public

• Use Cases: Interactive visualizations and dashboards.
• Features:
• Tableau Public allows you to create visually appealing and interactive data visualizations, including distribution curves.
• It has built-in statistical functions for normal distributions and percentiles.
• How: You can plot your data and use trend lines and distributions to overlay the normal curve.
• Note: Free version is available, though the data is public.

5. Google Data Studio

• Use Cases: Simple and interactive dashboards for data analysis.
• Features:
• Create interactive reports with charts like histograms and scatter plots.
• Though it may lack complex distribution options, it can handle percentile-based comparisons.
• How: Use histogram charts and combine with calculated fields for statistical measures like mean and standard deviation.
• Note: Fully web-based and free.

6. Gnuplot

• Use Cases: Command-line driven plotting software.
• Features:
• Excellent for generating various types of plots, including histograms and probability distributions.
• Customizable and able to handle large datasets.
• How: Generate histograms using the plot command and fit them with normal or binomial curves.
• Note: Lightweight and open-source.

Recommendations:

• For simplicity: Start with Google Sheets/Excel if you're working with small datasets and need basic distribution charts.
• For customization and larger datasets: Go with Jupyter Notebooks (Python) or RStudio. They offer more control over the visualization, including normal curve fitting and customization.
• For interactivity and presentation: Use Tableau Public or Google Data Studio for a more polished and shareable output.

Each of these tools can help you create the desired graphical profiles with normal distribution characteristics, allowing you to benchmark a candidate's relative standing effectively.