“Open to Work” Isn’t the Only Signal: Decoding Digital Body Language

Most recruiters know the power of LinkedIn’s “Open to Work” banner, but relying on a single flag is like reading only one chapter of a novel. In fact, ML/AI engineers leave their roles for a host of reasons that show up in subtle online behaviours: profile edits, promotion dates, endorsement patterns and more. This article unpacks the concept of digital body language, how engineers’ online footprints reveal their career intentions, and shows you how to combine multiple signals to predict and engage top talent in a way that’s both timely and tailored.

1. What Is Digital Body Language?

Digital body language refers to the constellation of online actions, such as profile updates, activity spikes, skill endorsements, that serve as behavioural proxies for a candidate’s readiness to explore new opportunities. Van Esch et al. (2017) coined the term to capture how “profile edits” and “status updates” act like non-verbal cues in recruitment.

• Profile Edits: Adding new skills or projects
• Activity Spikes: Posting, liking or commenting on domain content
• Endorsements & Connections: Sudden upticks in peer validation
• Timeline Events: Promotion dates or new role start dates

Rather than a blunt instrument, these signals form a nuanced picture of candidate momentum and when interpreted correctly, they can boost your response rates by focusing outreach on engineers genuinely open to dialogue.

2. Why “Open to Work” Alone Falls Short

LinkedIn’s “Open to Work” toggle is the most visible signal, but it captures only a fraction (~15%) of engineers about to switch roles. In our Bay Area sample:

• 15.1% display “Open_to_Work” within 90 days of leaving
• Yet 29.2% actually changed employers within six months 

This gap means that over half of imminent job-seekers fly under the “Open to Work” radar. Relying solely on that flag risks missing:

1. Quiet passives updating their headline or adding a new certification
2. Recently promoted engineers who may churn once the euphoria of promotion fades
3. Funding-event-driven movers whose timing aligns with startup liquidity, not LinkedIn toggles

3. Empirical Foundations: What the Data Tells Us

Our analysis of 2,830 Bay Area ML/AI engineer profiles uncovers two standout behavioural predictors:

Hazard Ratio (HR) quantifies how much more likely someone is to leave at any point in time (Cox model).

Odds Ratio (OR) measures likelihood of switching within six months (Logistic regression).

Importantly, other timeline events matter too:

• Promotion Lag: Engineers promoted within 6 months are 18% less likely to switch (OR = 0.82).
• Employer Change Lag: If they started their current role < 6 months ago, churn risk is lower, until the 6-month mark passes.

Together, these findings demonstrate that behavioural signals, when read in combination, offer a far richer early-warning system than “Open to Work” alone.

4. Integrating Multiple Signals into Your Workflow

To decode digital body language effectively, follow a three-layered approach:

1. Baseline Flags
   • Open_to_Work_Inferred (< 90 days since toggle)
   • Recent Profile Edits (skills added, new projects featured)
2. Timeline Triggers
   • Promotion Dates: Trigger a re-engagement cadence at 180 days post-promotion.
   • Employer Change Anniversary: Notify at 6 months, when retention risk spikes.
3. Contextual Modifiers
   • Funding Events: For startup alumni, schedule outreach 30–60 days after major funding rounds.
   • Education & Tenure Tiers: Tailor messaging for PhD holders vs. mid-level MScs (they respond to different value propositions).

By layering these signals, you can compute a Candidate Readiness Score, a simple index that weights each behavioural cue (e.g., 3 pts for Open_to_Work, 2 pts for recent profile edit, –1 pt for promotion < 180 days), and rank your outreach queue accordingly.

5. Practical Playbook: From Signals to Outreach

Step 1: Weekly Scan
– Pull all ML/AI profiles with either Open_to_Work_Inferred = 1 or profile edits in the past 30 days.

Step 2: Score & Segment
– Assign points for each signal; segment into:

• Hot Leads (score ≥ 4)
• Warm Leads (score 2–3)
• Watchlist (score 1)

Step 3: Tailored Messaging

Step 4: Feedback & Iterate
– Track reply, interview and offer rates by segment
– A/B test subject lines (“Equity-driven AI roles” vs. “Senior ML engineer growth path”)
– Refine signal weights based on real-world conversion

Conclusion

Digital body language transcends the blunt “Open to Work” indicator, unlocking a multidimensional portrait of candidate readiness. By combining profile edits, timeline triggers and contextual signals, backed by robust cohort analysis, you’ll shift from scattergun outreach to laser-focused engagement. The result? Faster time-to-fill, higher quality pipelines and a reputation as a recruiter who truly understands what makes ML/AI engineers click.

What You Can Test Next

• Implement a small-scale pilot: run your new scoring system on 200 ML engineers and measure improvement in InMail reply rates.
• Compare cohorts reached via single-signal (“Open to Work”) vs. multi-signal outreach, track conversion lift.