SourcingNav — Company Dashboard

Org Health Dashboard

Real-time pulse of your engineering organization

Total Headcount

47

↑ 5 this quarter

12-Mo Retention

93%

↑ 2% from last quarter

Avg Tenure

2.1y

→ stable

eNPS Score

+42

↑ 8 from last survey

Headcount Trend

Growth over the last 12 months

Attrition by Quarter

Voluntary vs. involuntary departures

Action Items

3 urgent

Flight Risk: Sarah Kim (Staff MLE)

Comp below P50, no promo in 18 months, manager 1:1s missed 3x. Flight risk model: 72%.

Risk Score: HIGHAction: Schedule skip-level this week

Onboarding: 2 new hires need Day-30 check-in

Alex Reyes (Data Eng) and Mia Torres (ML Eng) both started March 8. First milestone review due.

Due todayOnboarding health: both green so far

Promotion cycle: 6 candidates ready for review

Q2 promo packet deadline is April 15. Engineering has 6 nominees — 3 have complete packets.

7 days remaining3 packets incomplete

Team Composition

Recent Hires

4 in last 60 days

Name	Role	Start	Status
Alex Reyes	Data Engineer	Mar 8	● Day 30
Mia Torres	ML Engineer	Mar 8	● Day 30
Dev Patel	Sr. Agent Eng	Feb 20	● Day 47
Clara Song	Analytics Eng	Feb 3	● Day 64

Open Roles

6 active

Role	Team	Days Open	Candidates
Staff ML Engineer	Training	45d	3
Engineering Manager	Platform	32d	5
ML Eval Engineer	Evals	18d	2
Data Engineer	Infra	12d	8
Agent Engineer	Agents	8d	4
Junior MLE	Training	5d	12

Team Roster

47 people across 5 teams · hover for health signals

Employee	Role	Team	Level	Tenure	Health	Last 1:1	Flight Risk
SC Sarah Chen VP Engineering	VP Eng	Leadership	L8	3.2y	● Thriving	Apr 5	Low
JL James Liu EM — Training Pod	Eng Manager	Training	L7	2.8y	● Healthy	Apr 7	Low
SK Sarah Kim Staff MLE	Staff MLE	Training	L6	2.1y	● At Risk	Mar 15	72%
MR Marcus Rivera Senior MLE	Sr MLE	Training	L5	1.5y	● Healthy	Apr 6	Low
DP Dev Patel Sr. Agent Engineer	Sr Agent Eng	Agents	L5	47d	● Onboarding	Apr 4	Low
WZ Wei Zhang Senior Data Eng	Sr Data Eng	Infra	L5	1.8y	● Watch	Apr 2	38%
AR Alex Reyes Data Engineer	Data Eng	Infra	L4	30d	● Onboarding	Apr 1	Low
CS Clara Song Analytics Engineer	Analytics Eng	Evals	L4	64d	● Growing	Apr 3	Low

Onboarding Tracker

4 people currently onboarding · 30/60/90 day milestone tracking

Onboarding Now

4

Avg Time-to-Productivity

38d

↑ 5d faster than benchmark

30-Day Satisfaction

4.6

↑ out of 5.0

Buddy Assignments

4/4

AR

Alex Reyes

Data Engineer · Infra Team · Day 30

● On Track

Day 1 — Mar 8

First day setup

Laptop, accounts, Slack channels, buddy assignment (Wei Zhang)

Day 7

First PR merged

Bug fix in data pipeline — fast start signal

Day 30 — Today

30-Day Check-in

Review: onboarding experience, team integration, first project scope

Day 60

Ownership milestone

Owns at least 1 pipeline end-to-end

Day 90

Full ramp complete

On-call ready, independent contributor, peer feedback collected

MT

Mia Torres

ML Engineer · Training Team · Day 30

● On Track

Day 1 — Mar 8

First day setup

Paired with buddy Marcus Rivera. GPU access provisioned.

Day 14

First training run

Successfully fine-tuned a small model on internal data — strong signal

Day 30 — Today

30-Day Check-in

Review: technical ramp, team fit, identify first project ownership

Day 60

Project ownership

Owns eval pipeline for one model variant

Day 90

Full ramp

Ships production feature independently, peer calibration

Onboarding Health Metrics

Employee	Day	First PR	Buddy Score	Manager Score	Self Score	Overall
Alex Reyes	30	Day 7 ✓	4.5	4.3	4.8	4.5
Mia Torres	30	Day 14 ✓	4.7	4.5	4.2	4.5
Dev Patel	47	Day 5 ✓	4.8	4.6	4.4	4.6
Clara Song	64	Day 10 ✓	4.3	4.4	4.5	4.4

Retention Intelligence

Flight risk detection, satisfaction tracking, and churn prevention

12-Mo Retention

93%

↑ industry avg: 85%

Flight Risk (High)

2

⚠ needs attention

Avg Engagement

4.2

↑ out of 5.0

Regrettable Attrition

1

→ last 12 months

Flight Risk Monitor

Predictive model based on comp, growth, engagement, and behavioral signals

🔴 Sarah Kim — Staff MLE · 72% flight risk

Signals: Comp at P35 (should be P55+), passed over in last promo cycle, 1:1 with manager missed 3 consecutive weeks, updated LinkedIn profile last week, tenure at 2.1y (typical departure window).

→ Recommended: Skip-level with VP this week. Prepare retention offer: comp adjustment to P55 + promotion timeline to L7.

🟡 Wei Zhang — Sr Data Eng · 38% flight risk

Signals: Expressed interest in ML work during last 1:1 but role is pure data eng, comp is fair (P50), engagement score dropped from 4.5→3.8 over 2 quarters.

→ Recommended: Discuss ML rotation or hybrid project. Internal mobility is cheaper than replacement.

🟢 Remaining 45 employees — average risk 8%

No other high-risk signals detected. 6 employees in the "watch" band (15-25%) — flagged for proactive 1:1 focus.

Retention by Tenure Band

Where people leave — and why

Flight Risk Factors — Correlation with Actual Departures (Last 24 Months)

Growth & Development

Career progression, skill development, and promotion readiness

Promo Rate (Annual)

22%

↑ healthy for eng

Avg Time-in-Level

1.8y

Promo Candidates (Q2)

6

IDPs Completed

78%

↑ from 65% last Q

Promotion Readiness — Q2 2026 Cycle

6 candidates · Packet deadline: April 15

Employee	Current	Target	Time in Level	Manager Rec	Peer Signal	Packet	Readiness
Marcus Rivera	L5	L6 (Staff)	1.5y	Strong Yes	4.7/5	Complete	92%
Diana Torres	L4	L5 (Senior)	2.0y	Strong Yes	4.5/5	Complete	88%
Ryan Park	L5	L6 (Staff)	1.2y	Yes	4.2/5	Complete	75%
Aisha Okafor	L4	L5 (Senior)	1.8y	Yes	3.9/5	Incomplete	62%
Kevin Wu	L3	L4 (Mid)	1.1y	Yes	4.1/5	Incomplete	58%
Lisa Nguyen	L5	L6 (Staff)	2.3y	Maybe	4.0/5	Not Started	35%

Level Distribution

Org Competency Map

Team-level strengths and gaps across 14 competencies

Competency Heat Map — Team × Competency

Org-Wide Strengths

System Design

4.4

Production Eng

4.2

Project Execution

4.1

Velocity

4.0

Org-Wide Gaps

Evaluation Design

2.0

Mentorship

2.5

Stakeholder Influence

2.6

Written Comm

2.8

Insight: Evaluation Design is a critical gap — you're hiring an ML Eval Engineer (open role #3), which will help. Mentorship gap suggests seniors are over-indexed on IC work and need protected time for coaching.

Employee Engagement

Pulse surveys, eNPS tracking, and sentiment analysis

eNPS Score

+42

↑ excellent

Engagement Index

78%

↑ 5% from Q4

Survey Participation

91%

1:1 Completion Rate

87%

↓ target: 95%

Engagement Trend (Quarterly)

Engagement by Category

Manager Quality

4.4

Career Growth

3.6

Comp & Benefits

3.3

Work-Life Balance

4.0

Mission Alignment

4.6

Team Collaboration

4.3

Watch: Comp & Benefits at 3.3 is the lowest category. Correlates with Sarah Kim's flight risk. Consider mid-year comp review for below-P50 employees.

Workforce Planning

Headcount plan, budget allocation, and growth roadmap — 47 → 60 by EOY

Hiring Roadmap — 2026

Q1 (Done) — 5 Hires

Foundation hires completed

2 Data Eng, 1 Sr Agent Eng, 1 Analytics Eng, 1 ML Eng

Q2 (Now) — 6 Open Roles

Scale the platform and agent teams

Staff MLE, EM, ML Eval Eng, Data Eng, Agent Eng, Jr MLE

Q3 — 4 Planned

Research and evaluation buildout

2 Research Scientists, 1 Sr Eval Eng, 1 ML Platform Eng

Q4 — 3 Planned

Leadership and scale

1 EM (Agents), 1 Sr MLE, 1 Staff Data Eng

EOY Target

60

+13 from current · $3.8M additional comp budget

Team Size Targets

Team	Current	Q2 Target	EOY Target	Gap
Training	12	14	16	+4
Platform / Infra	10	12	14	+4
Agents	8	10	12	+4
Evals / Research	5	6	9	+4
Leadership	4	5	5	+1
Engineering Total	39	47	56	+17
Other (PM, Design, etc)	8	8	4	—

Budget Allocation by Team

Hiring Pipeline

6 open roles · 34 candidates in process · avg 28 days to hire

Open Roles

6

Candidates Active

34

Avg Time-to-Hire

28d

↑ 5d faster than last Q

Offer Accept Rate

82%

Pipeline by Role

Role	Sourced	Screen	Interview	Final	Offer	Days Open	Status
Staff ML Engineer	12	5	3	1	0	45d	Slow
Engineering Manager	18	8	5	2	1	32d	In Offer
ML Eval Engineer	8	4	2	0	0	18d	On Track
Data Engineer	22	10	4	2	0	12d	Strong
Agent Engineer	15	6	3	0	0	8d	On Track
Junior MLE	35	12	0	0	0	5d	New

Hiring Funnel — Last 90 Days

Candidates in Pipeline

34 active · Resume benchmarks · Interview prep status

All Interviewing Final Round Offer

Candidate	Role	Stage	Fit Score	Strengths	Gaps	Prep Status	Next Step
Marcus Rivera Staff ML · Ex-Meta	Staff ML Engineer	Final Round	4.4	System Design PyTorch Scale	Agent Frameworks	Prep Sent	System design panel · Thu 2pm
Priya Sharma Sr. MLE · Ex-Stripe	Staff ML Engineer	Interview	3.8	ML Infra Python	Team Size LLMs	Pending	Technical screen · Mon 11am
James Wu EM · Ex-Databricks	Engineering Manager	Offer Extended	4.6	People Mgmt ML Ops Hiring	None flagged	Complete	Awaiting response · deadline Fri
Sofia Gonzalez ML Eng · Ex-Google	ML Eval Engineer	Interview	4.1	Eval Design A/B Testing	Small Team Exp	Prep Sent	Manager interview · Wed 3pm
David Kim Data Eng · Ex-Snowflake	Data Engineer	Final Round	4.3	Spark dbt Airflow	Streaming	Prep Sent	Team match panel · Tue 1pm
Rachel Adams Sr. DE · Ex-Uber	Data Engineer	Interview	3.6	Kafka Python	dbt Cloud-Native	Pending	Technical screen · Wed 10am
Alex Tanaka Agent Dev · Ex-LangChain	Agent Engineer	Interview	4.5	LangChain Agents RAG	None flagged	Prep Sent	Live coding · Thu 10am

Candidate Deep Dive — Marcus Rivera

Staff ML Engineer · Final Round · System design panel Thursday

STRONG CANDIDATE

Resume Benchmark

Technical Depth

92

Seniority Signal

88

Scope of Impact

85

Role Alignment

78

Culture Indicators

80

Skill Match vs. Role Req

1.0 PyTorch · exact · production

1.0 Python · exact

1.0 System Design · exact · L6 signal

1.0 Distributed Training · exact

0.6 LangChain · adjacent to agent frameworks

0.0 Agent Eval Frameworks · gap

Interview Prep Material

System Design Panel

Probe: ML training pipeline at scale (Meta experience). Ask about failure modes in distributed PyTorch. Push on agent orchestration — this is the gap area.

Interviewer: Sarah K. · Thu 2pm

Culture & Values

Previous role: 200-person ML org → our team is 47. Assess comfort with ambiguity, willingness to do IC work at staff level, cross-functional communication style.

Interviewer: Director · Thu 3pm

Comp & Close Strategy

Current: ~$380K TC at Meta. Our range: $350-420K. Likely needs top-of-band + signing bonus. Competing offer from Anthropic (verbal). Move fast.

Decision: offer by Friday EOD

Hiring Panel Recommendation: Strong hire. Technical depth exceeds bar for Staff ML. Only gap is agent framework experience — mitigatable given strong distributed systems foundation. Flight risk: competing Anthropic offer. Recommend extending offer by Friday with top-of-band comp + $40K signing bonus. Retention signal: wants to build from scratch (positive for our stage).

People Analytics

Cost metrics, diversity data, and organizational health indicators

Cost-per-Hire

$18K

↑ 12% below budget

Time-to-Productivity

38d

Regrettable Turnover

2.1%

↑ well below 5% target

Manager Span

6.2

→ healthy range (5-8)

Diversity Metrics

Gender Diversity

36%

Underrep. Minority

28%

Women in Leadership

30%

International

24%

Note: Gender diversity in ML/AI teams averages 22% industry-wide. At 36%, we're above benchmark. Continue sourcing diversity-focused pipeline for open roles.

Compensation Distribution

Competitor Scan

Scanning competitor career portals for roles, comp ranges, and hiring velocity in your market

Competitors Tracked

23

Open Roles Found

187

↑ 14 new this week

Roles Overlapping Ours

32

Avg Comp vs. Ours

+8%

competitors pay more

sourcingnav scan --mode=company --industry=ai-ml --location=sf,remote

COMPETITOR PORTAL SCAN — 23 companies · AI/ML industry · SF + Remote ═══════════════════════════════════════════════════════════ DIRECT COMPETITORS (same roles you're hiring for) Anthropic 12 ML roles $240-340K ↑↑ surge hiring OpenAI 9 ML roles $250-360K ↑ growing Databricks 8 ML roles $220-300K → stable Scale AI 6 ML roles $200-280K ↑ growing Cohere 4 ML roles $210-290K → stable ⚠ ALERT: Anthropic posted 3 Agent Engineer roles this week. Direct competition for our open Agent Engineer req. Their comp is 15% above ours. ADJACENT COMPETITORS (talent overlap) Stripe ML 5 roles $220-310K fintech ML overlap Vercel AI 3 roles $200-280K infra overlap Figma ML 2 roles $210-270K applied ML overlap Scan complete. 12 new roles since last scan (3 days ago).

Competitor Role Comparison

Your open roles vs. competitor postings for the same positions

Your Open Role	Your Comp Range	Competitor	Their Comp	Delta	Their JD Quality	Threat Level
Staff ML Engineer	$300-380K	Anthropic	$320-420K	-10%	A	HIGH
Agent Engineer	$220-280K	OpenAI	$250-320K	-15%	A	HIGH
ML Eval Engineer	$200-260K	Scale AI	$210-270K	-4%	B+	MED
Data Engineer	$190-240K	Databricks	$200-250K	-5%	B	MED
Engineering Manager	$280-350K	Cohere	$260-320K	+8%	C+	LOW
Junior MLE	$140-180K	No direct match	—	—	—	LOW

Action Required: Staff ML Engineer and Agent Engineer roles are 10-15% below market at Anthropic/OpenAI. Consider adjusting comp bands or adding differentiators (equity upside, scope, team size, mission) to stay competitive.

Batch Candidate Evaluation

Side-by-side candidate comparison with ranked scoring across all open roles

sourcingnav batch --role="Staff ML Engineer" --candidates=4

BATCH EVALUATION — Staff ML Engineer · 4 candidates ═══════════════════════════════════════════════════════════ RANKED RESULTS RANK CANDIDATE FIT TECH CULTURE GROWTH VERDICT ───────────────────────────────────────────────────────────────── #1 Marcus Rivera 4.4 4.6 4.0 4.5 STRONG HIRE #2 Priya Sharma 3.8 4.0 4.2 3.5 HIRE #3 Kevin Park 3.4 3.8 3.0 3.5 MAYBE #4 Lisa Torres 2.8 2.5 4.0 2.0 PASS HEAD-TO-HEAD: #1 vs #2 Dimension Marcus Rivera Priya Sharma ───────────────────────────────────────────────── Technical Depth 4.6 ████████ 4.0 ██████░░ System Design 4.8 ████████ 3.5 █████░░░ Culture Fit 4.0 ██████░░ 4.2 ███████░ Leadership Signal 4.5 ███████░ 3.0 ████░░░░ Growth Trajectory 4.5 ███████░ 3.5 █████░░░ Comp Alignment 3.5 █████░░░ 4.5 ███████░ → RECOMMENDATION: Extend offer to Marcus. Priya is strong backup. Marcus wins on 4/6 dimensions. Priya has better comp alignment and stronger culture signals — consider for future Agent Eng role.

Marcus Rivera

Staff ML · Ex-Meta · 8 years exp

#1 STRONG HIRE

Technical Depth

4.6

System Design

4.8

Culture Fit

4.0

Growth Trajectory

4.5

Strengths: Distributed training at Meta scale, system design for 200M+ param models, strong leadership signal

Gap: No agent framework experience — mitigatable with strong distributed systems background

Priya Sharma

Sr. MLE · Ex-Stripe · 6 years exp

#2 HIRE

Technical Depth

4.0

System Design

3.5

Culture Fit

4.2

Growth Trajectory

3.5

Strengths: ML infrastructure at Stripe scale, excellent culture fit, strong cross-functional communication

Gaps: Smaller team experience (team of 8), less LLM-specific work — would need ramp time

Market Intelligence

Competitor landscape, comp benchmarks, and talent flow — your hiring hygiene report

Competitors Tracked

23

Their Open ML Roles

187

↑ 14 new this week

Your Comp vs Market P50

-8%

below market

JD Quality Score

B+

room to improve

Compensation Hygiene Check

Your posted ranges vs. market P50 — are you competitive?

Your Role	Your Range	Market P25	Market P50	Market P75	Grade	Action
Staff ML Engineer	$300-380K	$280K	$320K	$380K	B+	Competitive at top-of-band
Agent Engineer	$220-280K	$230K	$270K	$320K	C+	Raise floor to $240K
Engineering Manager	$280-350K	$260K	$300K	$340K	A-	Strong — well above P50
ML Eval Engineer	$200-260K	$190K	$230K	$270K	B	Competitive mid-range
Data Engineer	$190-240K	$180K	$215K	$250K	A-	Above P50 — strong
Junior MLE	$140-180K	$135K	$160K	$185K	A	Excellent range

Talent Flow — Where ML Talent Goes

Last 90 days · LinkedIn data

You're Losing Talent To

Anthropic

4

OpenAI

2

Databricks

1

You're Winning Talent From

Meta AI

5

Amazon ML

3

Scale AI

2

Your Posting Quality Audit

How your JDs compare to top competitors

Comp Transparency

A

Role Clarity

B+

Tech Stack Specificity

A-

DEI Language

C+

SEO Optimization

C

JD Length (optimal: 600-800 words)

B

Quick Wins: Add inclusive language to Agent Engineer JD (currently scores C+ on DEI). Optimize all JDs for Google Jobs SEO — add salary structured data and location tags.

Market Comp Trends — Senior ML Engineer (P50)

Hiring Calibration Engine

Learning from past hiring outcomes to improve your interview signal and reduce bad hires

Hires Tracked

18

Interview-to-Hire Rate

21%

↑ 3% improvement

Regrettable Hires

2

both below 3.5 score

Score Accuracy

84%

↑ from 71% last year

sourcingnav calibrate --mode=company --outcomes=18

HIRING CALIBRATION ANALYSIS — 18 hires tracked (12 thriving, 4 adequate, 2 regrettable) ═══════════════════════════════════════════════════════════ INTERVIEW SCORE → PERFORMANCE CORRELATION Score Range Thriving Adequate Regrettable Success Rate ────────────────────────────────────────────────────── 4.5+ 6 0 0 100% 4.0 – 4.4 5 2 0 71% 3.5 – 3.9 1 2 0 33% Below 3.5 0 0 2 0% ⚠ INSIGHT: Your hiring bar of 3.5 is too low. Every hire below 3.5 has been regrettable. Raise bar to 4.0. INTERVIEWER SIGNAL QUALITY ✓ Sarah K. (tech screen): 91% signal accuracy — best interviewer ✓ Director panel: 85% signal accuracy — strong ~ Peer coding (avg): 62% signal accuracy — needs calibration ✗ Culture fit panel: 48% signal accuracy — weak predictor → RECOMMENDATION: Retrain culture fit interviewers. Current panel passes candidates who don't perform. Replace with structured behavioral. INTERVIEW PROCESS PATTERNS ✓ Take-home → onsite: 88% correlation with actual performance ✗ Whiteboard coding: 41% correlation with actual performance ✓ References (structured): 79% correlation Calibration complete. Recommendations saved to hiring_calibration.yml

What Predicts Success at Your Company

System design (real-world)

91%

Take-home project quality

88%

Prior startup experience

79%

Structured references

79%

Pedigree / brand name

35%

Whiteboard coding

41%

Action Items from Calibration

Raise Hiring Bar → 4.0 minimum

Every hire scored below 3.5 became regrettable. 3.5-3.9 has only 33% success. Set 4.0 as the floor.

Retrain Culture Fit Panel

48% signal accuracy is coin-flip territory. Switch to structured behavioral interview with rubric.

Drop Whiteboard Coding

41% correlation with performance. Replace with take-home (88% correlation) for all roles.

Integrations

ATS, HRIS, and market data connections feeding your talent engine

🌿

Greenhouse

● Connected

ATS — Syncing jobs, candidates, scorecards, and interview schedules every 30 minutes.

Last sync3 min ago

Jobs synced6

Candidates34

Stage changes today5

🎋

BambooHR

● Connected

HRIS — Employee data, org chart, comp data, time-off, and performance reviews.

Last sync1 hr ago

Employees synced47

Comp records47

Reviews pending3

🌐

Google Jobs (SerpAPI)

● Connected

Market intel — Scanning competitor postings for comp ranges, role velocity, and market trends.

Last scanToday 6:00 AM

Competitors tracked23

Roles indexed187

Available Integrations

Connect additional data sources

Platform	Type	What You Get	Cost	Status
Merge.dev	Universal ATS/HRIS	Connect 50+ platforms through one API	Free (3 accounts)	Recommended
Rippling	HRIS	Employee data, payroll, benefits, device mgmt	Free (API)	Not connected
LinkedIn Jobs	Intel	Competitor headcount, posting data, talent flow	Proxycurl ($50/mo)	Not connected
Levels.fyi	Comp Data	Real-time compensation benchmarks by company/level	API ($99/mo)	Not connected
Lever	ATS	Alternative ATS — candidates, opportunities, offers	Free (API)	Not connected
Lattice	Performance	Performance reviews, goals, 1:1 notes, engagement surveys	Free (API)	Not connected