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workflow automation

  1. Simple Workflows:
  2. Page Day Workflow: Showcase a daily routine that involves automating content creation or updates. For instance, how AI can be used to auto-generate daily content or manage social media posts.

  3. List Day Workflow: This could include automation for compiling, updating, and publishing various top lists. Highlight how AI can help in data aggregation, sorting, and visualization.

  4. Complex Workflows:

  5. Contract Management Workflow: Demonstrate a more complex process such as managing and automating tasks outside IR35 contracts. This might include AI-driven tools for contract analysis, risk assessment, and compliance checks.

For each workflow, you can provide a detailed guide, potential tools and technologies used (like PyTorch for AI tasks), and the benefits of automating these processes. This not only showcases your expertise but also provides practical solutions that businesses can implement.

Contract Management Workflow

To automate the process of securing a contract outside IR35 for roles like AI consultant, technical project manager, and delivery manager, you can set up a streamlined workflow that helps you track opportunities and manage applications efficiently. Here's a simple five-step automated workflow for your "Page a Day" section:

  1. Daily Job Fetching:

  2. Automate the process of fetching new job listings from multiple sources like LinkedIn, Indeed, and niche job boards for tech and AI roles. Use APIs or web scraping tools to gather new postings daily.

  3. Filtering and Sorting:

  4. Automatically filter these listings based on keywords such as "AI consultant," "technical project manager," "delivery manager," and "outside IR35." Rank these opportunities based on relevance and potential fit.

  5. Application Prepping:

  6. Use a template system to automatically generate customized cover letters and tweak your resume to match the job description keywords. This could involve AI tools that analyze the job description and suggest the most relevant projects and skills from your portfolio.

  7. Application Submission:

  8. Automate the submission process where possible, or set up reminders and pre-filled forms to streamline manual submissions. Ensure that each application is tracked for follow-up.

  9. Follow-Up and Tracking:

  10. Implement a CRM or use a simple tracking tool like Trello or Airtable to monitor the status of each application. Automate follow-up emails or set reminders for personal follow-ups to increase your chances of landing an interview.

For the optional technical writer role, you might focus on highlighting your ability to simplify complex technical topics, which could be an asset in any of the three primary roles as well.

Contract Management Workflow incement 1

Absolutely, Mat! Here’s a basic outline of how you might structure your Python script to automate fetching job postings from Gmail, analyzing them for specific keywords, and then organizing them in a Google Sheet for further action:

  1. Fetch Emails from Gmail:

  2. Use the imaplib and email libraries in Python to connect to your Gmail account and fetch unread emails from a specific folder designated for job alerts.

  3. Extract Job Details:

  4. Parse each email to extract relevant job details like the position title, description, and any other pertinent information. This step would involve cleaning and structuring the data.

  5. Analyze Keywords and Categorize Roles:

  6. Utilize simple text processing or a more advanced natural language processing library like nltk to identify keywords and categorize the job as AI consultant, technical PM, delivery manager, or technical writer based on the content.

  7. Push Data to Google Sheets:

  8. Use the gspread library to connect to a Google Sheet where you can store and manage your job applications. Each row in the sheet could represent a job, with columns for details like role, keywords, matched CV, application status, etc.

  9. Manage Applications and Follow-Ups:

  10. The Google Sheet acts as your CRM, where you manually update the application status as you apply, and track responses and next steps.

  11. Analysis for Continuous Improvement:

  12. Periodically analyze the data in your sheet to identify trends, such as which keywords are most successful in getting responses, or which type of roles are yielding better results. This can inform how you tweak your CV and cover letters.

Here’s a simple snippet to get you started on the first part – fetching emails from Gmail:

import imaplib
import email
from email.header import decode_header

# Connect to Gmail
username = 'your_username'
password = 'your_password'
mail = imaplib.IMAP4_SSL('imap.gmail.com')
mail.login(username, password)
mail.select("inbox")  # Connect to inbox.

# Search for specific emails
type, data = mail.search(None, 'FROM', "job_alerts@example.com")
email_ids = data[0].split()

# Fetch and parse emails
for email_id in email_ids:
    _, data = mail.fetch(email_id, '(RFC822)')
    for response_part in data:
        if isinstance(response_part, tuple):
            msg = email.message_from_bytes(response_part[1])
            subject, encoding = decode_header(msg["subject"])[0]
            if isinstance(subject, bytes):
                subject = subject.decode(encoding)
            print("Subject:", subject)
            # You can parse date, sender, and other headers similarly.

# Be sure to log out from mail
mail.logout()

Contract Management Workflow incement 2

Certainly! Let's break down the process a bit further and expand on how each part of the script will work, moving from fetching emails to processing and organizing the job data:

  1. Fetching Emails:

  2. The Python script starts by connecting to your Gmail account using imaplib. It then searches for and fetches emails from a specific sender or folder, which you can customize based on how you receive job alerts.

  3. Parsing Emails:

  4. Once the emails are fetched, the script parses each email to extract the subject and body. The body of the email, where the job description and details are likely contained, can be processed to extract relevant information like job title, description, and other specifics.

  5. Keyword Analysis and Categorization:

  6. Using text processing techniques, the script analyzes the content of the job description to identify key terms related to the roles you're interested in (AI consultant, technical project manager, etc.). This step can be enhanced by using a library like nltk for more sophisticated natural language processing.

  7. Storing Data in Google Sheets:

  8. After categorizing and extracting the key data from each job posting, the script uses gspread to connect to Google Sheets and store this information. Each job can be a new row in your spreadsheet with columns for job title, description, detected keywords, and your application status.

  9. Manual Interaction and Follow-Up:

  10. With the data in Google Sheets, you can manually review each job, decide if you want to apply, and update the sheet with your application status. You can also set reminders for follow-ups.

  11. Analysis for Improvements:

  12. Periodically, you can analyze the spreadsheet to find trends in the job market, the success rate of your applications, and adjust your CV and cover letter accordingly.

Here’s how you might extend the script to push data to Google Sheets, assuming you've already set up a Google Sheet with appropriate API access:

import gspread
from oauth2client.service_account import ServiceAccountCredentials

# Setup Google Sheets connection
scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']
creds = ServiceAccountCredentials.from_json_keyfile_name('client_secret.json', scope)
client = gspread.authorize(creds)
sheet = client.open("Job Applications").sheet1

# Assuming you have job details as a dictionary
job_details = {
    'Title': 'AI Consultant',
    'Description': 'Responsible for developing AI solutions...',
    'Keywords': 'AI, Machine Learning, Python',
    'Status': 'Not Applied'
}

# Append job details to the sheet
sheet.append_row(list(job_details.values()))

This overview should give you a good idea of how the complete system will function, from fetching the job postings to processing and organizing them for your application process. If you'd like to delve into any specific part further, let me know!