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Job Summary

Responsible for developing and deploying machine learning algorithms. Evaluates accuracy and functionality of machine learning algorithms. Translates application requirements into machine learning problem statements. Analyzes and evaluates solutions both internally generated as well as third party supplied. Develops novel ways to use machine learning to solve problems and discover new products. Has in-depth experience, knowledge and skills in own discipline. Usually determines own work priorities. Acts as resource for colleagues with less experience.

Job Description

About the Role:

We are seeking an experienced Data Scientist to join our growing 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, 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 curious minds 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

Design and tune machine learning models for event correlation, anomaly detection, alert scoring, and root cause inference
Engineer features to enrich alerts using service relationships, business context, change history, and topological data
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
Participate in incident postmortems, providing ML-driven insights and recommendations for platform improvements
Collaborate on the design of hybrid ML + rule-based systems to supportnamic correlation and intelligent alert grouping
Lead and support innovation efforts including POCs, POVs, and explorationemerging AI/ML tools and strategies
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
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
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, confluence pages, white papers, presentations, test results, technical manuals, formal recommendations and reports. Contributes to the company by creating patents, Application Programming Interfaces (APIs).

Comcast is proud to be an equal opportunity workplace. We will consider all qualified applicants for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability, veteran status, genetic information, or any other basis protected by applicable law.

Base pay is one part of the Total Rewards that Comcast provides to compensate and recognize employees for their work. Most sales positions are eligible for a Commission under the terms of an applicable plan, while most non-sales positions are eligible for a Bonus. Additionally, Comcast provides best-in-class Benefits to eligible employees. We believe that benefits should connect you to the support you need when it matters most, and should help you care for those who matter most. That's why we provide an array of options, expert guidance and always-on tools, that are personalized to meet the needs of your reality – to help support you physically, financially and emotionally through the big milestones and in your everyday life. Please visit the compensation and benefits summary on our careers site for more details.

Education

Bachelor's Degree

While possessing the stated degree is preferred, Comcast also may consider applicants who hold some combination of coursework and experience, or who have extensive related professional experience.

Relevant Work Experience

5-7 Years

Data Scientist 3