Senior Data Scientist

Hewlett Packard Enterprise•Sunnyvale, CA

1d•Onsite

About The Position

Hewlett Packard Enterprise is a global edge-to-cloud company focused on advancing how people live and work by helping companies connect, protect, analyze, and act on their data and applications from edge to cloud. The company's culture emphasizes innovation, valuing diverse backgrounds, and providing flexibility for work-life balance. The Senior Data Scientist role is an onsite position primarily based at an HPE office. This role involves data science research and software application development related to HPE's AI Datacenter technology and autonomous platform, aiming to enhance visibility and operational efficiency in user experience. The Senior Data Scientist will collaborate with other engineers to build next-generation autonomous Datacenter networks using big data and predictive models. The role leverages network data to empower the inference engine of the Mist platform and systems, including the Mist virtual assistant chatbot. Responsibilities include developing and implementing scalable algorithms using knowledge of network communication, machine learning, and software engineering to process large amounts of streaming data for anomaly detection, problem prediction, Root Cause Analysis (RCA), and real-time classification. The Senior Data Scientist will also develop software and algorithms to enhance cloud intelligence for Marvis.

Requirements

Bachelor's degree in Computer Science/ Engineering/Mathematics or equivalent experience
5+ years of experience Search Indexing, Ranking, Information Retrieval and Querying
Proficient in Python and Golang
Proficient in implementing NLP, Machine Learning models and algorithms into production at scale
Solid statistics and math background, good knowledge of machine learning methods like k-Nearest Neighbors, Naive Bayes, SVM, Decision Forests
Excellent Communication Skills to articulate observations and use cases with PM and network domain experts who are not experienced in AI/ML through data visualization tool
Good understanding of datacenter networking topology and protocols
Knowledge of the multi-cloud production environment
Agility to troubleshoot open-source data processing engine, such as Apache Spark, Apache Storm and Apache Flink
Good knowledge and experience of the big data tool sets and techniques of distributed storage and computation engine
Experience to develop the reusable and highly scalable data processing component
Good knowledge and experience to work with cloud based CICD tools and cloud devops teams to collect stats and create monitors for our data processing pipelines
Good understanding of MCPs and Agentic frameworks

Nice To Haves

PhD degree in Statistics, Operations Research, Computer Science or equivalent and 5+ years of relevant experience
Master´s Degree in these areas and at least 8 years of relevant experience
Experience with statistical data analysis, data mining, and querying
Experience in deploying and leading complete ML platforms in AWS/GCP/Azure
Time series data analysis, forecasting and correlation is preferrable
Utilized latest AI/ML techniques, such as Neural Networks, Transformer, etc. for time series data or interested to explore these techniques for time series data
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

Responsibilities

Design and implement machine learning solutions which require to process terabytes of streaming data to detect anomalies in DC networks of our customers, predict problems and future trends, provide Root Cause Analysis (60%)
Troubleshoot production environment and customer reported issues (20%)
Utilize analytical and programming skills and open-source systems, such as Hadoop, Hive, Spark, Elasticsearch, Redis, etc. develop data processing pipeline required efficacy and latency (20%)

Benefits

Comprehensive suite of benefits that supports physical, financial and emotional wellbeing
Investment in career development with specific programs to help reach career goals
Unconditionally inclusive work environment that celebrates individual uniqueness
Flexibility to manage work and personal needs
Variable incentives may also be offered

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