First, never give your salary expectation to a recruiter unless you're working with them to find a role amongst several options. Why?
Companies will go higher for an exceptional candidate and the recruiter will screen you out of the process.
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Start VERY high and follow up with, "And where does that fall in this role's salary band?" If you're making $150K now, ask, "I am looking for $250K. Where does that fall in this role's salary band?"
Your mindset should be, what if they say yes? Salaries have risen more than data professionals expect.
Go into a negotiation with the new mindset, but the 2nd part of the statement is critical.
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The phrasing creates a hedge and starts the negotiation. Remember, negotiation means offer and counteroffer. When you are a strong candidate and start with a high offer, you have set the starting conditions for a negotiation.
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If they set the starting conditions, their number will probably be lower and you will be in the position of asking them to compromise. Starting high puts the business in the position of asking you to compromise.
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It also gives you the opportunity to say a magic line, "OK. I really want to work here and am willing to negotiate."
By starting high, you've established your number as the starting point for negotiations.
You are putting them in the position of asking you to compromise and you can ask for concessions in return.
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You're showing a tangible desire to work there backed up by a willingness to compromise.
Bonus: A high number creates a perception of value to reinforce your strength as a candidate. It would be contradictory for you to take a low number, right?
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So there it is. Start high. Hedge to allow for negotiations to start. Show your desire to work there by your willingness to compromise.
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What if they say yes? Great outcome.
What if they say no? Negotiations start and you get a better outcome.
What if they never call you back? They weren't that interested in the first place.
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#LinkedIn is the Botox of #socialmedia. It's all fake. My timeline's filled with corporate propaganda and reposts. I feel like I'm in a library and someone will tell me to keep it down if I post what I'm really thinking.
1/7
"Proud to be joining Google!" No, you're proud of that paycheck and you want your old company who wouldn't spring for a raise to feel it.
"No promotion, huh? Not good enough for a raise? Funny, GOOGLE thought I was!!!!! HAHAHAHA!!!!" Full send. 2/7
"It was a tough decision to leave my old company." No, you loved every second of writing your resignation and sending it to your idiot boss. The video I want to see is of you writing up that resignation email with a long slow-motion shot of your face when you hit send. 3/7
MIT Sloan - “The survey also found that AI yields strategic benefits, but they mostly accrued to companies that use AI to explore new ways of creating value rather than cutting costs.” Let me explain why that's critical.
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Translation: Our field is transitioning from cost savings to revenue generation. The business is looking for Data Scientists to lead the discovery of opportunities and deployment of new products.
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“Those that used AI primarily to create new value were 2.5 times more likely to feel that AI is helping their company competitively compared with those that said they are using AI primarily to improve existing processes”
3/11 #DataScience#ArtificialIntelligence#Strategy
If a Data Scientist has a Github with 3 Python projects, you don't need to give them a technical interview. If they've been working as a Data Scientist for 3+ years, they don't need a take-home project. 1/9 #DataScience#MachineLearning#Hiring
Do they have a blog with 1 or 2 years worth of posts on Machine Learning Engineering? Published research? A YouTube channel with tons of Data Science educational content? Significant open source contributions? 2/9 #DataScience#MachineLearning#Hiring
I get a better sense of a candidate's capabilities from those sources. In my experience, the generic methods have lower predictive value for employee performance. 3/9 #DataScience#MachineLearning#Hiring
What is the difference between a predictive problem and a causal inference problem? This is an essential differentiation for data scientists, and even very smart people botch the answer. 2 very smart authors did just that. Let me explain.
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They proposed 2 questions:
1. Should I hire more college graduates? 2. Should I subsidize college degrees for my employees?
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In the article, they said question 1 is a prediction problem and question 2 is a causal inference problem. They are both causal inference problems because they ask the data scientist to prescribe a policy.
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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.
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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.
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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.
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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.
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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.
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