AI/Machine Learning Engineer

Key Skills

Aartificial intelligence, AWS, AWS Sagemaker, Azure, Data Analytics, Data science techniques, Design, Effective communication skills, ETL frameworks, Generative AI, Github, Large Language Models - LLMs, Machine learning techniques, PySpark, SPARK Programming

Job Description

We are looking for an AI ML Data Engineer to help lead the technical architecture and strategy for PS Data Analytics & DS/ML initiatives.  In this role, you'll provide the technical leadership and advanced subject matter knowledge needed to solve complex business issues, you will help us advance our capability by demonstrating your passion for big data, data science, machine learning, software best practices and innovative solutions. 

Knowledge Skills: 

• Deep knowledge and experience in architecting, developing, and deploying large scale web services and platforms. 
• Expert knowledge of data modelling techniques and high-volume ETL design 

• Statistical emphasis on data mining techniques, Bayesian Networks Inference, CHAID, CART, Association Rule, Linear and non-linear regression, hierarchical mixed models/ multi-level modelling 
• Demonstrated ability to architect across multiple disciplines and design solutions involving Firmware, Software, Mobile, and cloud-based solutions. 
• Actively tracking advancements in the industry and offers from cloud services that HP should be capitalizing on.  Ability to make forward looking make vs buy decision leveraging a growing and changing industry. 

Education: 

• A Bachelor’s /master’s degree in computer science, Engineering, or similar level of applied research knowledge gained through industry experience.  

• 6+ years solving complex problems in cloud platforms, platform architecture and DS & ML strategies for businesses. 
• 4+ years of experience in handling BIG data using SPARK programming language. Experience working on AWS cloud services.
• Great communication skills to establish a vision, align executive leaders, convey complex concepts to teams with differing backgrounds, build trust, and create engaging partnerships across HP. 
• 4+ years of hands-on experience with detailed knowledge of data warehouse technical architectures, infrastructure components, ETL/ ELT and reporting/analytic tools.

Initiatives to work on immediately in the team.

Attach Rate Analysis and Enhancement (Analytics) 

• Analyze the data about the attach rate of accessories, including headsets, monitors, keyboards, and mice, to laptops and desktops. 
• Utilize different data sources such as PS device telemetry, Sales, NPS and Customer call data on customer purchasing behavior, trends, customer perception. 
• Develop predictive models that forecast attachment rates. Example: Returning Customer Model/Analysis, subscription opportunity Analysis. 

• Provide predictive models as requested by Poly, Hyper X and other peripheral business units.
• Monitor the effectiveness of these developed models and improve them to drive higher attachment rates and increased accessory sales. 

Software Updates and App Usage, Resource Analysis (Analytics) 

• Take the lead in analyzing app usage patterns and their impact on battery life and memory consumption in PCs and desktops.
• Collaborate with the product & software development teams and provide the analysis for more efficient application development.

Causality Modeling for NPS Improvement (AI/ML Modeling) 

• Develop causality models that enables deeper insights into the drivers of NPS.
• Identify actionable causal pathways by analyzing customer feedback, customer journeys, and touchpoints, seeking to pinpoint the most influential factors that impact NPS.
• Collaborate with product development & customer support teams to translate causality findings into product and customer support process improvements.

LLMs and GenAI in Device Telemetry and Time Series Data Analysis (AI/ML Modeling) 

• Participate & explore Large Language Models (LLMs) and Generative AI (GenAI) projects providing models to help internal team strategic initiatives.

Tools used: Azure AI sandbox, AWS Sagemaker, EMR clusters, AWS pyspark, Github Co-pilot.