Senior Software Data Engineer

at  Hewlett Packard

JOB SUMMARY

• This role is responsible for developing and maintaining logical data models in order to match business and client requirements. The role works with other team members to turn data into critical information and knowledge that can be used to make sound organizational decisions. The role proposes innovative ways to look at problems by using data mining approaches across a wide range and variety of data assets. The role focuses on predictive and prescriptive solutions, while pushing the boundaries of machine learning and artificial intelligence. The role applies unique mastery and recognized authority on relevant subject matter knowledge including technologies, theories and techniques.

Responsibilities-

• Understanding of current machine learning algorithms, under which circumstances each is applicable and their pros and cons. Able to compose new model architectures and objective functions. Able to integrate multiple models into systems.
• Knowledge of data processing techniques and data preprocessing requirements for the common machine learning approaches. Knowledge of data collection techniques. Knowledge of data augmentation approaches and their pros and cons.
• Leads organization wide team or teams of other data engineers for all stages of design and development for complex, secure and performant data solutions and models, including design, analysis, coding, testing, and integration of structured/unstructured data.
• Prepares and presents literature, presentations, invention disclosures for peer review & publication in industry data engineering domain initiatives and conferences.
• Assures designs and project activities are in compliance with data policies, architecture, security and quality guidelines and standards.
• Drives innovation and integration of new technologies into organization wide projects and activities in the big data space.
• Leverages recognized domain expertise, business acumen, and overall data systems leadership to influence decisions of executive business leadership, development partners, and industry standards groups.
• Represents the data engineering team for all phases of larger and more-complex development projects.
• Establishes scalable, efficient, and automated processes for large scale data analyses and model development, validation, and implementation.
• Explores the space of analytical opportunities for a given problem and executes with a sound analytical strategy.
• Builds and manages relationships throughout the organization.
• Provides guidance and mentoring to less experienced staff members.

Education & Experience Recommended

• Four-year or Graduate Degree in Computer Science, Information Technology, Software Engineering, Statistics/ Mathematics, or any other related discipline or commensurate work experience or demonstrated competence.
• Typically has 10+ years of work experience, preferably in data analytics, data engineering, data modeling, or a related field.

KNOWLEDGE & SKILLS

• Agile Methodology
• Python (Programming Language)
• SQL (Programming Language)
• Broad knowledge of machine learning, statistical modeling, or data mining
• Expertise with building robust systems based on machine learning techniques.
• Track record of work in Generative AI (LLMs, LVMs, LMMs) development and deployment.
• Knowledge of cloud deployments on AWS/Azure
• Experience leveraging models from repositories such as Hugging Face
• Experience with deep learning frameworks such as Pytorch and Tensorflow.
• Excellent Software development skills with fast prototyping in Python.

CROSS-ORG SKILLS

• Effective Communication
• Results Orientation
• Learning Agility
• Digital Fluency
• Customer Centricity

DISCLAIMER

• This job description describes the general nature and level of work performed in this role. It is not intended to be an exhaustive list of all duties, skills, responsibilities, knowledge, etc. These may be subject to change and additional functions may be assigned as needed by management.

HP offers a comprehensive benefits package, including:

• Dental insurance
• Disability insurance
• Employee assistance program
• Flexible schedule
• Flexible spending account
• Health insurance
• Life insurance

Our compensation reflects the cost of labor across several U.S. geographic markets, and we pay differently based on those defined markets. The typical base pay range for this role across the U.S. is $157,550 - $242650 annually with additional opportunities for pay in the form of bonus and/or equity. Pay within this range varies by work location and may also depend on job-related knowledge, skills, and experience. Your recruiter can share more about the specific salary range for the job location during the hiring process

• Understanding of current machine learning algorithms, under which circumstances each is applicable and their pros and cons. Able to compose new model architectures and objective functions. Able to integrate multiple models into systems.
• Knowledge of data processing techniques and data preprocessing requirements for the common machine learning approaches. Knowledge of data collection techniques. Knowledge of data augmentation approaches and their pros and cons.
• Leads organization wide team or teams of other data engineers for all stages of design and development for complex, secure and performant data solutions and models, including design, analysis, coding, testing, and integration of structured/unstructured data.
• Prepares and presents literature, presentations, invention disclosures for peer review & publication in industry data engineering domain initiatives and conferences.
• Assures designs and project activities are in compliance with data policies, architecture, security and quality guidelines and standards.
• Drives innovation and integration of new technologies into organization wide projects and activities in the big data space.
• Leverages recognized domain expertise, business acumen, and overall data systems leadership to influence decisions of executive business leadership, development partners, and industry standards groups.
• Represents the data engineering team for all phases of larger and more-complex development projects.
• Establishes scalable, efficient, and automated processes for large scale data analyses and model development, validation, and implementation.
• Explores the space of analytical opportunities for a given problem and executes with a sound analytical strategy.
• Builds and manages relationships throughout the organization.
• Provides guidance and mentoring to less experienced staff members