Senior Data Science Engineer (Infrastructure & Network Analytics)

Senior Data Science Engineer (Infrastructure & Network Analytics)

  

This role has been designed as ‘Hybrid’ with an expectation that you will work on average 2 days per week from an HPE office.

Job Description:

   

Job Family Definition:

Designs, develops and applies programs, methodologies and systems based on advanced analytic models (e.g. advanced statistics, operations research, computer science, process) to transform structured and unstructured data into meaningful and actionable information insights that drive decision making.
Uses visualization techniques to translate analytic insights into understandable business stories (eg. descriptive, inferential and predictive insights).
Embeds analytics into client’s business processes and applications. Combines business acumen and scientific methods to solve business problems.

Management Level Definition:

Contributions impact technical components of HPE products, solutions, or services regularly and sustainable. Applies advanced subject matter knowledge to solve complex business issues and is regarded as a subject matter expert. Provides expertise and partnership to functional and technical project teams and may participate in cross-functional initiatives. Exercises significant independent judgment to determine best method for achieving objectives. May provide team leadership and mentoring to others.

Role Overview

We are seeking a highly skilled Senior Data Science Engineer to drive the development of our next-generation Predictive Assurance and Real-Time Health Analytics platform. In this role, you will design, deploy, and optimize data pipelines, statistical algorithms and machine learning models that monitor, analyze, and forecast the health of our enterprise-grade routing fleet (including Juniper QFX Series nodes).

You will bridge the gap between heavy-duty Data Engineering and Advanced Machine Learning, implementing stateful batch analytics engines to detect insidious regressions like memory leaks, alongside deep learning models to predict physical hardware failures in our optical layer.

What You'll Do:

• Predictive Modeling: Design and refine time-series forecasting models (e.g., BiLSTM, Transformers, or Prophet) to predict optical performance and failure markers.

• Feature Engineering: Translate complex network telemetry (DOM metrics, FEC counters, BER, and thermal data) into actionable features for real-time anomaly detection.

• Distributed Computing & Data Pipelines: 5+ years of production experience with Apache Spark (PySpark/Scala) utilizing advanced windowing, state manipulation, and memory-efficient aggregations.

• Machine Learning Frameworks: Proven experience deploying LightGBM (or XGBoost) and Deep Learning frameworks (TensorFlow/Keras or PyTorch for LSTMs/BiLSTMs) into live production environments.

• Production Engineering: Lead the transition of models from R&D/Lab environments into our production Datacenter Assurance platform, ensuring scalability, low-latency, and high availability.

• Diagnostic Analytics: Develop statistical "Health Index" algorithms to identify currently degraded optics, moving beyond simple threshold alerts to intelligent, multivariate diagnostics.

• Collaboration: Partner with network hardware engineers and software architects to understand failure signatures and integrate data-driven insights into our monitoring workflows.

What You Need to Bring:

• Experience: 5+ years of professional experience in a Data Science, Machine Learning, or AI Engineering role.

• Core Skills: Expert-level proficiency in Python and deep learning frameworks (PyTorch or TensorFlow).

• Modeling: Strong background in time-series forecasting, anomaly detection, and multivariate analysis.

• Engineering: Production-level coding experience; familiarity with CI/CD, Docker, Kubernetes, and MLOps best practices.

• Data Handling: Proficiency with SQL and large-scale data processing frameworks (e.g., Apache Spark, Kafka); bonus skills - Apache Flink, Apache Storm

• Domain Knowledge: Familiarity with network telemetry, signal processing, or hardware performance metrics is a significant plus.

Preferred Skills:

• Experience with network monitoring systems or optical transceiver diagnostics.

• Expertise in statistical profiling (e.g., Z-score, Change-Point Detection, Dynamic Time Warping).

• Experience optimizing ML models for resource-constrained environments or high-throughput real-time systems.

Domain Knowledge:

• Infrastructure/Network Telemetry Domain: Experience working with time-series metrics generated by network devices, operating systems, or cloud infrastructure (e.g., RES/RSS memory components, process-level statistics, Junos/Linux kernel behavior).

• Optical Systems Familiarity: Basic understanding of fiber-optic or telecom infrastructure, specifically DOM (Digital Optical Monitoring) properties.

• MLOps Mindset: Experience with containerized deployment (Docker, Kubernetes) and model lifecycle tracking tools (MLflow, Kubeflow).

Education and Experience Required:

• PhD degree in Statistics, Operations Research, Computer Science or equivalent preferred and 3+ years of relevant experience. Or Master´s Degree in these areas and at least 5-6 years of relevant experience.

Accountability, Accountability, Action Planning, Active Learning, Active Listening, Agile Methodology, Agile Scrum Development, Analytical Thinking, Bias, Coaching, Creativity, Critical Thinking, Cross-Functional Teamwork, Data Analysis Management, Data Collection Management (Inactive), Data Controls, Design, Design Thinking, Empathy, Follow-Through, Group Problem Solving, Growth Mindset, Intellectual Curiosity (Inactive), Long Term Planning, Managing Ambiguity {+ 5 more}

Health & Wellbeing

We strive to provide our team members and their loved ones with a comprehensive suite of benefits that supports their physical, financial and emotional wellbeing.

Personal & Professional Development

We also invest in your career because the better you are, the better we all are. We have specific programs catered to helping you reach any career goals you have — whether you want to become a knowledge expert in your field or apply your skills to another division.

Unconditional Inclusion

We are unconditionally inclusive in the way we work and celebrate individual uniqueness. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good.

#india#networking

Job:

Engineering

Job Level:

TCP_04

    

    

Hewlett Packard Enterprise is EEO Protected Veteran/ Individual with Disabilities.

   

HPE will comply with all applicable laws related to employer use of arrest and conviction records, including laws requiring employers to consider for employment qualified applicants with criminal histories.

   

Recruitment Fraud Alert

We have become aware of an increase in fraudulent recruitment activities in which individuals impersonate our company or authorized recruitment agencies to offer fake employment opportunities. These scams may occur through false websites, emails, social media, or chat-based applications and often aim to obtain personal information or money. Please note that Hewlett Packard Enterprise (HPE), its direct and indirect subsidiaries and affiliated companies, and its authorized recruitment agencies/vendors will never charge a candidate a registration fee, hiring fee, or any other fee in connection with its recruitment and hiring process. We also never request personal information such as back account details, Social Security numbers, or national IDs via social media or chat applications.

All legitimate job opportunities will come through official company channels, and candidates are responsible for verifying the credentials of any third party claiming to represent the company. Any reliance on fraudulent communication is at the individual’s own risk, and HPE disclaims legal liability for any resulting damages. If you suspect recruitment fraud, do not share personal information or make any payments and report the incident to your local authorities immediately.