Data Operations Lead at halter

ABOUT HALTER At Halter, we’re on a mission to enable farmers and graziers to run the most productive and sustainable operations. Our customers are using Halter to break free from the time-intensive constraints of conventional practices. Imagine watching 500 cattle stand up and walk calmly towards their next break? No quad bikes, no dogs, no fences. Just a group of cattle walking at their own pace. People say it looks like magic. Our customers are revolutionizing grazing with Halter. It's changing lives and transforming an industry. People join Halter to do meaningful work. By joining us you’ll be solving challenging problems within a talented team and a culture built for high performance. Our team out-think, out-work and out-care. We’re committed to delivering real change in the world - this isn’t easy, and in truth, we love that it’s hard. We’re backed to deliver on a mission that matters by Tier 1 investors including Bessemer Venture Partners [Upgrade to PRO to see link] BOND, [Upgrade to PRO to see link] DCVC [Upgrade to PRO to see link] Blackbird [Upgrade to PRO to see link] Promus Ventures [Upgrade to PRO to see link] Rocket Lab’s Peter Beck and Icehouse ventures [Upgrade to PRO to see link] To find out more, visit our LinkedIn [Upgrade to PRO to see link] & Instagram [Upgrade to PRO to see link] ABOUT THE ROLE Data is in our DNA at Halter, and represents one of our highest leverage assets for delivering value to farmers and ranchers. The performance of Halter’s predictive models are hugely dependent on the quality ground truth data backing them. As such, we’re building a dedicated machine learning Data Operations function to own the full lifecycle of ground truth datasets: from collection of measurements with wide geographic diversity, through to annotation, and quality control—so that our ML teams can innovate with urgency, and ship reliable, high‑impact capabilities to farmers. WHAT YOU’LL DO You will design, build, and run repeatable, scalable ground-truth dataset building pipelines. The work blends operational leadership, data quality governance, and close partnership with ML engineering. GROUND TRUTH PROGRAMME OWNERSHIP - Own end-to-end ground truth programmes: define collection methodology, sampling strategy, annotation guidelines, review flows, and delivery milestones from concept to “model-ready” datasets. - Partner with ML engineers/researchers to translate model needs into label schemas, taxonomies, and measurable acceptance criteria (what “good” looks like). ANNOTATION OPERATIONS AND QUALITY CONTROL - Facilitate and execute plans to annotate data, using guidelines from client machine learning teams. - Build a quality control system to enable effective distribution of work, while maintaining high label quality. - Establish scalable QA processes that catch label noise early and reduce rework and downstream model risk. DATASET STRUCTURE, STEWARDSHIP, AND LIFECYCLE MANAGEMENT - Define dataset standards: metadata, versioning, provenance/lineage, update cadence, and “dataset contracts” for ML consumption. - Build lightweight but robust tooling and documentation so datasets are discoverable, reproducible, and safe to use. WORKFORCE AND VENDOR MANAGEMENT - Stand up flexible labour capability for collection/annotation—including vendor selection, onboarding/training, QA enforcement, throughput planning, and performance management. WHAT SUCCESS LOOKS LIKE In the first several months, this role is about building the machine—not only doing the work. - Within 90 days: a clearly defined ground-truth lifecycle (intake → collect → label → QA → version/release), initial label schema/taxonomy for priority ML problems, and baseline quality metrics. Comparable roles explicitly focus on defining standards and annotation frameworks early. - Within 6–12 months: predictable throughput and lead time, proven QA effectiveness (lower rework / higher agreement), and expanded regional capacity through repeatable vendor/contractor workflows—while maintaining a consistent quality bar. WHAT WE’RE LOOKING FOR MUST-HAVE EXPERIENCE - Demonstrated ownership of a process comparable to a data/annotation operation end-to-end (collection and/or labelling), including quality systems and delivery against schedules. - Comfort translating ambiguous ML needs into precise operational specs: label definitions, edge-case handling, escalation rules, and measurable acceptance criteria. - Capability to move with urgency: Of the Halter Operating principles, moving with urgency is particularly relevant. Data collection is one of the most laborious phases of model development. The faster it moves, the faster we can deliver value to farmers and ranchers. - Strong service mindset: you are passionate about making the cross-functional system work, and passionate about the success of your client teams (ML engineering teams). STRONGLY PREFERRED - Experience managing third‑party workforces (BPO, crowd, field contractors) and enforcing quality at scale—either as a client-side owner or within an annotation/data services provider. - Familiarity with annotation tooling and workflows (e.g., CVAT/Label Studio/Labelbox-style concepts), and dataset versioning/metadata practices. NICE-TO-HAVE - Exposure to real-world sensor data, computer vision, time series, or edge deployments (relevant to “ground truth measurement collection” and field variability). - Experience in highly regulated or safety-critical environments (healthcare, autonomy, defence) where data quality and provenance are non-negotiable JOIN OUR TEAM Halter is committed to promoting a diverse and inclusive workplace — a place where we can each be ourselves and do the best work of our lives. Research shows that while men apply to jobs when they meet an average of 60% of the requirements, women and under-represented groups of candidates tend to only apply when they meet every requirement. If you think you have what it takes but don’t necessarily tick every requirement on this job description, please still get in touch and apply to Halter. We’d love to chat to see if you’ll be an epic fit! If this opportunity sounds like you, please apply below by sending through your cover letter explaining why you’re excited about this role and working at Halter, along with your CV, and we’ll be in touch! Please also feel free to check out the careers page [Upgrade to PRO to see link] for more information on working at Halter and don't forget to follow us on LinkedIn [Upgrade to PRO to see link] & Instagram [Upgrade to PRO to see link] WHY OUR TEAM LOVES WORKING AT HALTER: - Work that genuinely matters. Every now and again a company comes along that transforms an entire industry and leaves the world in a better place. Our team gets to be part of something truly meaningful, helping farmers improve their livelihoods, spend more time with their families, and build more sustainable operations. - Spectacular people solving hard problems. Our culture is designed for talented people to do work that changes lives. The team is filled with diverse, kind, and driven people who push each other to do their best work. You'll be thrown into the deep end, tackling complex challenges and building something tangible that solves real problems. - You'll grow here. Autonomy, mastery, and learning define how we work. You'll have the freedom to work on interesting problems, master new skills, and continuously develop yourself, both through your role and our $1,000 personal growth fund. - This isn't easy, and we love that it's hard. Working at Halter will be the most rewarding and the most challenging work of your life. We move fast, take bold bets, and work hard to reshape an entire industry. As one team member put it: "Joining Halter is a bit like strapping yourself to a rocket ship, but it's an epic journey to be a part of!"