Vin Vashishta Profile picture
May 7 14 tweets 11 min read
Data Scientist Job Openings On LinkedIn:
March - 138K
Now - 134K

Hiring is slowing for mid to junior-level roles. That's the first sign of tightening budgets and more changes will come quickly. Let me explain what comes next.
1/14
#DataScience #MachineLearning #Leadership
Higher costs are compressing margins for businesses across industries. Revenue growth has stagnated. Both factors mean businesses must find ways to cut costs or they are in danger.
2/14
#DataScience #MachineLearning #Leadership
Missing on revenue projections or lowering guidance for the rest of the year is a death sentence for share prices. The C Suite is measured by share price so they're moving quickly to cut costs.
3/14
#DataScience #MachineLearning #Leadership
Data Science reduces costs through intelligent automation and creates new revenue streams with innovative products and features. The team is a necessity but also very expensive and often does not meet the business need.
4/14
#DataScience #MachineLearning #Leadership
According to an IDC survey, 69% of teams haven't matured enough to bring models to production. Changes in leadership are coming to data science teams that are cost centers.
5/14
#DataScience #MachineLearning #Leadership
New leaders will be brought in to reduce costs and focus the team on quick-win projects. Teams will reorganize.

New leaders will cycle out team members who can't execute or aren't necessary to support a leaner project schedule.
6/14
#DataScience #MachineLearning #Leadership
We're already seeing early layoff cycles and there will be more to come.

As budgets tighten, businesses prioritize roles with the highest ROI. Which roles are safe and can help you move forward during the next few years?
7/14
#DataScience #MachineLearning #Leadership
Data and ML Engineer hiring GREW by 4K each, reflecting businesses' new priorities. The focus on production drives the need for software engineering capabilities and these 2 roles will benefit from it.
8/14
#DataScience #MachineLearning #Leadership
Companies are looking at automation to help offset the need for Data and ML Engineers. There's a shortage of talent and these roles are expensive. Solutions will be adopted but they won't reduce demand for 2+ years.
9/14
#DataScience #MachineLearning #Leadership
Senior++ Data Scientist roles aren't seeing a slowdown in hiring either. Value-focused Data Scientists with a track record of delivering to production will thrive.
10/14
#DataScience #MachineLearning #Leadership
Companies need Data Scientists with domain expertise and business acumen. Adoption is a critical success factor. Data Scientists who understand their users and stakeholders execute better than those who don't.
11/14
#DataScience #MachineLearning #Leadership
The field also desperately needs Data Science Leaders. Demand and compensation will continue to rise for managers all the way up to CDOs. This is a new unicorn role.
12/14
#DataScience #MachineLearning #Leadership
Tangential roles like Machine Learning Product Manager drive value creation. Data Scientist Strategist, Data Librarian, Model QA, and other emerging roles will see significant growth and job security.
13/14
#DataScience #MachineLearning #Leadership
Make a move toward value creation, engage with the business and users, focus on monetization. Businesses keep people who consistently deliver revenue and cost savings. If you can do that, there are opportunities for you everywhere.
14/14
#DataScience #MachineLearning #Leadership

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More from @v_vashishta

May 6
New hiring rules. Any test given to a candidate has to be taken by the existing team, and 80% of them have to pass it.

1/11
#DataScience #MachineLearning #Hiring
If the job description asks for a minimum of 5 years of experience, it needs to include an explanation of why 4 years isn’t enough.
2/11
#DataScience #MachineLearning #Hiring
After 2 rounds of interviews, the company needs to explain what additional information they expect to get from this round and why they didn’t get it during the last round.
3/11
#DataScience #MachineLearning #Hiring
Read 11 tweets
May 5
Data Scientists looking for a new role and Recruiters looking for candidates speak 2 different languages. Miscommunication is the most common reason candidates disengage, drop out of the interview process, and reject offers. Why?
1/12
#DataScience #Recruiting #Hiring
Candidates eventually find out the role isn’t what they expected and there's not way to keep them involved in the process after that.
2/12
#DataScience #Recruiting #Hiring
Explaining a role to a Machine Learning Engineer vs. Data Engineer vs. Applied Researcher vs. Generalist Data Scientist vs. Data Analyst are all different conversations.
3/12
#DataScience #Recruiting #Hiring
Read 12 tweets
May 4
Supervised deep learning is limited by label quality. An ontology must be built before labeling begins. That's a graph defining concepts and their connections. Ontologies guide labeling to ensure consistency and completeness.
1/5
#DataScience #MachineLearning #DeepLearning
Any problem space including people introduces multiple, often conflicting ontologies. Datasets ideally have multiple labels and require multiple models to be trained.
2/5
#DataScience #MachineLearning #DeepLearning
Most projects have a single, majority consensus labeling methodology. Where ontologies diverge from or conflict with it, inference will be inaccurate no matter how incredible the models we use become.
3/5
#DataScience #MachineLearning #DeepLearning
Read 5 tweets
May 4
Approach your Data Science learning path strategically. Start by asking, ‘why do people build models?’ I'm going to explain a more effective approach to learning our field that focuses on applications over theory.
1/10
#DataScience #MachineLearning #CareerAdvice
Most use cases in the business world don’t use complex machine learning or deep learning. It’s mostly analytics and simple models.

Why do people build simple models? Models are mathematical tools to extract knowledge from data.
2/10
#DataScience #MachineLearning #CareerAdvice
Why do people build datasets? Datasets introduce new knowledge into the business. Having data is not enough. The dataset must contain new knowledge.
3/10
#DataScience #MachineLearning #CareerAdvice
Read 10 tweets
Apr 26
Open-sourcing Twitter’s algorithm isn’t what most people think it is. I don’t think even Elon Musk or most people at Twitter really understand where this process goes.
1/10
#DataScience #MachineLearning #Twitter
The code is not very insightful. The model itself is too complex for people to understand and interact with. So, what does open-sourcing the algorithm look like?
2/10
#DataScience #MachineLearning #Twitter
It’s the ability to click on a Tweet in your timeline and get a detailed explanation of why it was served to you. There are levels of model explainability.
3/10
#DataScience #MachineLearning #Twitter
Read 10 tweets
Apr 25
How will companies move into the Metaverse? Most platform-based businesses are already there. Google, Amazon, and Facebook are all platform native companies so they have a clear lane into the Metaverse.
1/7
#Metaverse #Strategy
Their businesses have always been digital-first and built on a platform with access to a business ecosystem or marketplace. Building an increasingly capable platform grew their accessible ecosystems.
2/7
#Metaverse #Strategy
Platforms remove barriers to scale so a company like Amazon could disrupt and rapidly take market share from retail incumbents. Google and Facebook entered emerging, very small ecosystems-Google for search and Facebook for social.
3/7
#Metaverse #Strategy
Read 7 tweets

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