Data Scientist 2

Comcast brings together the best in media and technology. We drive innovation to create the world's best entertainment and online experiences. As a Fortune 50 leader, we set the pace in a variety of innovative and fascinating businesses and create career opportunities across a wide range of locations and disciplines. We are at the forefront of change and move at an amazing pace, thanks to our remarkable people, who bring cutting-edge products and services to life for millions of customers every day. If you share in our passion for teamwork, our vision to revolutionize industries and our goal to lead the future in media and technology, we want you to fast-forward your career at Comcast.
**Job Summary**

**Core Responsibilities**
- 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
- 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 support dynamic correlation and

intelligent alert grouping
- Lead and support innovation efforts including POCs, POVs, and exploration of emerging 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
- 3+ 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 Infrastruct