Staff Data Scientist – Performance Marketing
Que
PythonSQLTableau
Architect, Data Scientist, Data & AI Team
JAGGAER
full-time
Posted on:
Location: North Carolina • 🇺🇸 United States
Visit company websiteJob Level
SeniorLead
Tech Stack
AirflowAmazon RedshiftAWSAzureDockerERPETLGoGoogle Cloud PlatformIoTKubernetesLinuxOpen SourcePandasPySparkPythonPyTorchRedisSQLTableauTensorflowTerraform
About the role
- Data & Feature Engineering: Build scalable ingestion, ETL/ELT, and feature store pipelines across OpenSearch, Snowflake, Redshift, and Redis.
- Design semantic layers and vector indexes (Pinecone, OpenSearch) that power retrieval augmented generation (RAG) and Agentic AI workflows.
- Model Development & Experimentation: Prototype, train, and evaluate predictive, prescriptive, and generative models in Amazon SageMaker (plus open source frameworks).
- Implement automated A/B tests and champion/challenger experiments; translate findings into product requirements.
- ML / LLM Ops: Own CI/CD, monitoring, drift detection, and scalable inference for classical ML and LLM pipelines.
- Package models and agents into reusable micro services with Terraform / Docker / Kubernetes.
- Agentic Platform Integration: Orchestrate multi-agent task flows (LangGraph, CrewAI, or equivalent) that call JAGGAER and third-party APIs.
- Collaborate with front-end teams to embed real-time analytics and AI insights into customer-facing apps.
- Insight Generation & Storytelling: Diagnose customer data issues; deliver visual analyses (Tableau, Superset, Streamlit, or R/Python) for executives and non-technical stakeholders.
- Champion data-driven decision making across Product, Services, and Go to Market teams.
Requirements
- Bachelor’s or Master’s in Computer Science, Statistics, Math, Data Science, or related field.
- 10+ years designing and deploying production-grade ML or data engineering solutions.
- Proficiency in Python (Pandas, PySpark, scikit learn, TensorFlow/PyTorch) and SQL.
- Hands-on work with at least two of the following platforms: OpenSearch, Snowflake, Redshift, Redis, Pinecone, SageMaker.
- Solid grounding in statistical modeling, supervised/unsupervised ML, and evaluation metrics.
- Experience with Linux, Git, CI/CD, Docker, and at least one orchestration framework (Airflow, Prefect, Kubeflow, or Dagster).
- Clear, concise communicator able to present complex analyses to senior leadership.
- Preferred: LLM fine tuning, prompt engineering, or RAG pipelines; experience deploying ML services on AWS/Azure/GCP; knowledge of procurement, supply chain, IoT sensor, or ERP data; familiarity with LangChain, CrewAI, Haystack; track record of hackathons/open-source/published research.
