Job Details

Data Quality Manager

Location: Remote (Chicago area, IL)

Employment type: Full-time

Step into the driver's seat of an AI-first industrial tech disruptor that is redefining how factories see, think, and act. As the Data Quality Manager, you will architect the data pipelines that power state-of-the-art computer-vision models - turning raw imagery into production-grade insight. You will collaborate with machine-learning engineers, product leaders, and offshore labeling teams, owning both strategy and hands-on execution. Your work will have direct, measurable impact on product accuracy, customer adoption, and revenue growth.

Position Snapshot

• Domain: High-volume image/video data for vision AI solutions in manufacturing and robotics
• Team: Cross-functional ML engineering group (100 % remote, U.S. time-zones)
• Influence: First hire fully dedicated to data quality - build processes, choose tools, and scale vendor operations
• Compensation: Competitive base salary + bonus/equity, comprehensive benefits, and equipment stipend
• Travel: None (occasional team off-sites optional)

Key Responsibilities

• Direct the full lifecycle of large-scale computer-vision datasets - from raw capture and stratified sampling to release-ready benchmark packages and performance sign-off.
• Write and maintain annotation playbooks, class taxonomies, and version-controlled guidelines, managing change logs for every update.
• Lead external annotation partners and internal reviewers, setting throughput goals, quality KPIs, and daily calibration sessions.
• Design multi-layer QC workflows - consensus voting, blind audits, gold-task seeding, and IAA monitoring - to surface issues before they hit production.
• Partner with ML engineers on error analysis and active-learning loops, mining hard examples to boost model precision.
• Implement dataset lineage and versioning (e.g., DVC/lakeFS), producing immutable manifests for every release.
• Publish weekly dashboards covering label accuracy, coverage, review latency, and SLA attainment, driving continuous improvement.
• Champion diversity and bias mitigation, ensuring balanced, representative data for fair model performance.
• Collaborate with customer stakeholders to define edge cases, success metrics, and rollout roadmaps - communicating status, risks, and mitigation plans.
• Evaluate and extend best-in-class tooling (Label Studio, CVAT, etc.), integrating QC signals into the existing MLOps stack.

Qualifications

Must-haves

• Proven leadership of image/video annotation programs at production scale with strong vendor-management skills.
• Expertise in data-quality science - consensus/aggregation methods, IAA metrics, sampling, and gold-task programs.
• Hands-on experience with leading annotation platforms and custom workflow scripting.
• Proficiency in SQL and Python for data inspection, automation, and reporting.
• Stellar communication skills - able to translate complex model requirements into clear labeling instructions and QC checks.

Nice-to-haves

• Active-learning and data-augmentation strategy experience.
• Familiarity with label-aggregation algorithms and probabilistic labeling.
• Experience setting up data-versioning/lineage tools and integrating with MLOps dashboards (Weights & Biases, ClearML, etc.).
• Background in industrial or safety-critical environments is a plus.

Why You'll Love It Here

• Impact from Day 1 - Your decisions shape the accuracy and trustworthiness of every model shipped.
• Growth Path - Build the entire data-quality function with a trajectory toward data-ops leadership.
• Cutting-Edge Tooling - Work with leading open-source and commercial platforms while driving innovation in vision AI.
• Mission-Driven Team - Collaborate with world-class engineers who value curiosity, ownership, and excellence.

About Blue Signal:

Blue Signal is an award-winning, executive search firm specializing in various specialties. Our recruiters have a proven track record of placing top-tier talent across industry verticals, with deep expertise in numerous professional services. Learn more at bit.ly/46Gs4yS

1. The Core Purpose of the Role

This isn't a generic "data management" position - it's specifically focused on computer-vision data quality for AI/ML systems in industrial or manufacturing environments.

That means the primary mission is to:

Ensure that every pixel of visual data used to train AI models is accurate, consistent, diverse, and traceable - so that models can make reliable real-world decisions.

This is a high-leverage role because the quality of labeled images and videos directly affects model performance, product accuracy, and ultimately customer satisfaction.

⚙ 2. What Kind of Person You're Looking For

A. Functional Expertise

You're seeking someone who:

• Has led large-scale image/video annotation programs, not just text/tabular data.
• Understands data labeling science - especially consensus methods, inter-annotator agreement (IAA), and gold standard testing.
• Is hands-on with tools like Label Studio, CVAT, or SuperAnnotate, and can write scripts (Python, SQL) to automate QA or analysis.
• Can interface with ML engineers and understand feedback loops between labeling and model training.

So, look for candidates who can talk about:

• Dataset lifecycle management
• Quality control (QC) workflows
• Error analysis and model validation
• Vendor management (since this involves offshore labeling teams)

B. Leadership and Process-Building

Because this is the first dedicated data-quality hire, you want someone who can:

• Design the entire data-quality system - playbooks, taxonomy, and workflows from scratch.
• Manage external vendors and set KPIs.
• Scale operations while maintaining consistency and fairness in data.
• Communicate effectively between engineers, product managers, and customers.

This is both strategic (build systems) and tactical (run checks, write scripts).

C. Cultural and Domain Fit

Given the company's positioning ("AI-first industrial tech disruptor"), you're targeting:

• People who have worked at AI startups, robotics companies, or industrial vision firms.
• Candidates comfortable in fast-paced, ambiguous environments who take ownership.
• Those motivated by impact - building foundational systems that directly improve model trust and adoption.

3. How to Identify a Strong Match (Signals to Look For)

CategoryWhat to Look ForRed FlagsExperienceManaged image/video labeling pipelines (not just data entry or BI)Generic data governance, no mention of computer visionToolsFamiliar with Label Studio, CVAT, or similar; uses Python for QC scriptsLimited to Excel or TableauLeadershipHas led vendor teams or annotation contractorsOnly worked as an annotator or QC reviewerML AwarenessUnderstands how labeling impacts precision/recall metricsTalks about "accuracy" in vague business termsMindsetCurious, process-driven, collaborativeToo rigid, slow to adapt, or lacks communication clarity

4. How to Pitch It (if you're recruiting for this)

If you're talking to candidates, emphasize:

• "You'd be the architect of our entire data-quality operation."
• "Your work directly affects model accuracy - this is high-impact, not background ops."
• "We're at the intersection of AI and manufacturing - your systems will touch real robots and vision models in production."

That tends to resonate strongly with technically minded data professionals who want ownership and impact.