System Analyst 3

About the Role: 

We are seeking an experienced Sr. System Analyst to join our growing Global Operational Intelligence team. You will play a key role in building intelligent systems that help reduce alert noise, detect anomalies, correlate events, and proactively surface operational insights across our large-scale streaming infrastructure. 

You’ll work at the intersection of machine learning, artificial intelligence, observability, and IT operations, collaborating closely with Platform Engineers, SREs, Incident Managers, Operators and Developers to integrate smart detection and decision logic directly into our operational workflows. 

This role offers a unique opportunity to push the boundaries of AI/ML in large-scale operations. We welcome futuristic and innovative mindsets who want to stay ahead of the curve, bring innovative ideas to life, and improve the reliability of streaming infrastructure that powers millions of users globally. 

What You’ll Do: 

• Analyze, Design and tune machine learning models for big data processing through a multitude of system analysis methods aligning with our design patterns in a cloud environment (AWS, Google, Azure) 

• System Testing and Quality Assurance with oversight of quality engineering 

• Apply NLP and ML techniques to classify and structure logs and unstructured alert messages 

• Develop and maintain real-time and batch data pipelines to process alerts, metrics, traces, and logs 

• Use Python, SQL, and time-series query languages (e.g., PromQL) to manipulate and analyze operational data 

• Collaborate with engineering teams to deploy models via API integrations, automate workflows, and ensure production readiness 

• Contribute to the development of self-healing automation, diagnostics, and ML-powered decision triggers 

• Design and validate entropy-based prioritization models to reduce alert fatigue and elevate critical signals 

• Conduct A/B testing, offline validation, and live performance monitoring of ML models 

• Build and share clear dashboards, visualizations, and reporting views to support SREs, engineers, and leadership 

• Research and diagnose complex application problems and identifying system improvements in an enterprise environment. 

• Testing the system on a regular basis to ensure quality and function while, writing instruction manuals for the systems 

• Collaborate on the design of hybrid ML/AI + rule-based systems to support dynamic correlation and intelligent alert grouping 

• Document business process and change algorithms for continuous improvements for assessing complexity in patterns 

• Preparing cost benefit analysis on systems platform, and feature and the value chain attributed to the deployed feature and providing recommendations on features that are not used. 

• Demonstrate a proactive, solution-oriented mindset with the ability to navigate ambiguity and learn quickly 

• Participate in on-call rotations and provide operational support as needed 

Qualifications: 

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Statistics or a related field 

• 5+ years of experience building and deploying ML solutions in production environments 

• 2+ years working with AIOps, observability, or real-time operations data 

• Strong coding skills in Python (including pandas, NumPy, Scikit-learn, PyTorch, or TensorFlow) 

• Experience working with SQL, time-series query languages (e.g., PromQL), and data transformation in pandas or Spark 

• Familiarity with LLMs, prompt engineering fundamentals, or embedding-based retrieval (e.g., sentence-transformers, vector DBs) 

• Strong grasp of modern ML techniques including gradient boosting (XGBoost/LightGBM), autoencoders, clustering (e.g., HDBSCAN), and anomaly detection 

• Experience managing structured + unstructured data, and building features from logs, alerts, metrics, and traces 

• Familiarity with real-time event processing using tools like Kafka, Kinesis, or Flink 

• Strong understanding of model evaluation techniques including precision/recall trade-offs, ROC, AUC, calibration 

• Comfortable working with relational (PostgreSQL), NoSQL (MongoDB), and time-series (InfluxDB, Prometheus) databases, GraphDB 

• Ability to collaborate effectively with SREs, platform teams, and participate in Agile/DevOps workflows 

• Clear written and verbal communication skills to present findings to technical and non-technical stakeholders 

• Comfortable working across Git, Confluence, JIRA, & collaborative agile environments 

Nice to Have: 

• Experience building or contributing to the AIOps platform (e.g., Moogsoft, BigPanda, Datadog, Aisera, Dynatrace, BMC etc.) 

• Experience working in streaming media, OTT platforms, or large-scale consumer services 

• Exposure to Infrastructure as Code (Terraform, Pulumi) and modern cloud-native tooling 

• Working experience with Conviva, Touchstream, Harmonic, New Relic, Prometheus, & event-based alerting tools 

• Hands-on experience with LLMs in operational contexts (e.g., classification of alert text, log summarization, retrieval-augmented generation) 

• Familiarity with vector databases (e.g., FAISS, Pinecone, Weaviate) and embeddings-based search for observability data 

• Experience using MLflow, SageMaker, or Airflow for ML workflow orchestration 

• Knowledge of LangChain, Haystack, RAG pipelines, or prompt templating libraries 

• Exposure to MLOps practices (e.g., model monitoring, drift detection, explainability tools like SHAP or LIME) 

• Experience with containerized model deployment using Docker or Kubernetes 

• Use of JAX, Hugging Face Transformers, or LLaMA/Claude/Command-R models in experimentation 

• Experience designing APIs in Python or Go to expose models as services and/or GraphQL 

• Cloud proficiency in AWS/GCP, especially for distributed training, storage, or batch inferencing 

• Contributions to open-source ML or DevOps communities, or participation in AIOps research/benchmarking efforts 

• Certifications in cloud architecture, ML engineering, or data science specializations