Anaplan
SaaS
Manager,SoftwareDevelopment
Neural analysis suggests this role is
optimal for Lead candidates.
“Manager, Software Development at Anaplan. Skills: Machine Learning, ML Pipelines, Cloud Platforms, Data Stores. Lead ML pipeline design. Develop production-grade ML pipelines”
What You'll Achieve.
customers can outpace their competition; deliver classification-driven intelligence; deliver reliable, scalable predictive capabilities; making classification models production-ready at enterprise scale
Industry & Context.
optimizing business decision-making; translate customer needs into reliable, scalable predictive capabilities
What They're Looking For.
Must Have
Python, scikit-learn, Pandas, NumPy, PyTorch, TensorFlow, classification models, feature selection, imbalanced-class handling, calibration, explainability, SHAP, LIME, ML pipelines, orchestration, Airflow, Prefect, MLOps, model lifecycle management, versioning, registry, A testing, canary deployments, monitoring, alerting, Helm, resource quotas, HPA, service mesh, MongoDB, document-oriented data modelling, query optimisation, aggregation pipelines, schema evolution, Redis, caching layer, real-time feature store, message broker, Streams, pub/sub, AWS, Azure, GCP, object storage, managed databases, container registries, monitoring, DevOps practices, Docker, GitHub Actions, automated testing, GitOps workflows, represent technical decisions, globally distributed organisation
Nice to Have
Master's or Ph. D. in Computer Science, Statistics, or a closely related quantitative field, probabilistic classification, uncertainty quantification, calibrated probabilities, conformal prediction, Apache Spark, Dask, SaaS, enterprise platform environments, finance, supply chain, planning domains, Elasticsearch, Cassandra, open-source ML tooling, published research in applied ML
What You'll Do.
Lead ML pipeline design
Develop production-grade ML pipelines
Deploy production-grade ML pipelines
Define end-to-end ML lifecycle
Establish coding standards
Establish design-review practices
Establish engineering benchmarks
Drive architectural decisions
Conduct technical deep-dives
How You'll Work.
Team & Collaboration
Serve as technical authority for cross-functional squad; Work closely with Product; Work closely with Data Science; Collaborate with global counterparts; Lead diverse team; Value Backend perspectives; Value Frontend perspectives; Value QA perspectives
Communication Scope
Represent technical decisions to non-technical stakeholders
Full Job Description
At Anaplan, we are a team of innovators focused on optimizing business decision-making through our leading AI-infused scenario planning and analysis platform so our customers can outpace their competition and the market. What unites Anaplanners across teams and geographies is our collective commitment to our customers’ success and to our Winning Culture. Our customers rank among the who’s who in the Fortune 50. Coca-Cola, LinkedIn, Adobe, LVMH and Bayer are just a few of the 2,400+ global companies who rely on our best-in-class platform. Our Winning Culture is the engine that drives our teams of innovators. We champion diversity of thought and ideas, we behave like leaders regardless of title, we are committed to achieving ambitious goals, and we love celebrating our wins – big and small. Supported by operating principles of being strategy-led, values-based and disciplined in execution, you’ll be inspired, connected, developed and rewarded here. Everything that makes you unique is welcome; join us and let’s build what’s next - together! About the Role Anaplan is seeking a strong Tech Lead to join our Predictive Insights team — the group responsible for designing, building, and operating the machine learning systems that deliver classification-driven intelligence across Anaplan's Connected Planning platform. In this role, you will serve as the technical authority for a cross-functional squad of Backend, Frontend, and QA engineers, guiding everything from model design and data engineering to production deployment and observability. You will own the end-to-end lifecycle of classification models and ML pipelines: from prototyping in Python and feature-store integration, through containerised deployment on Kubernetes, to real-time serving backed by Redis and MongoDB. As a hands-on leader you will set architectural standards, mentor engineers at all levels, and work closely with Product and Data Science to translate customer needs into reliable, scalable predictive capabi
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