Data Science Engineer

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

D&TO has an opening for an Engineer in the area of Machine Learning (ML). We are looking for engineers who are passionate about Machine Learning and want to make an impact by creating ML solutions that completely change how HP does business today. The engineer will work with a very talented team towards the development of ML solutions that impact Commercial, Finance, and many other different organizations inside HP, collaborating with teams across the globe.

Applies developed subject matter knowledge to solve common and complex business issues within established guidelines and recommends appropriate alternatives. Works on problems of diverse complexity and scope. May act as a team or project leader providing direction to team activities and facilitates information validation and team decision making process. Exercises independent judgment within generally defined policies and practices to identify and select a solution. Ability to handle most unique situations. May seek advice in order to make decisions on complex business issues.

Responsibilities:

• Develops and programs integrated software solutions, especially in support of the development, deployment and life cycle of machine learning models including MLOps.
• Applies machine learning and statistical modeling techniques to business or research problems. Collects and analyzes data sources; develops, trains and evaluates models; creates visualizations of data properties and model performance. Deploys and maintains models
• Works closely with business stakeholders and platform providers to gather requirements, understand business use case, review the current MLOPs practices/ modelling approaches and suggest improvements across engineering best practices, recommended tools and model building approaches
• Work with business use cases for machine learning to instantiate engineering best practices and industry standards on platforms, to maximize business value and savings
• Keeps knowledge and skills current by reading state of the art research papers and blog posts from industry and research labs. Studies new methods to understanding industry trends and emerging technologies.

Knowledge and skills:

• Fluent in one or more Machine Learning Frameworks (e.g. Pytorch, TensorFlow, scikit-learn, etc). Fluent in Python and knowledge of one or more additional programming Languages, such as C++, R, C#
• Knowledge of modern computer science including algorithms, data structures, software architecture. Where applicable, knowledge of cloud and hybrid cloud service architectures and their impacts on development and deployment
• Knowledge of the Mathematics of Machine Learning (particularly Linear Algebra, differential calculus) and Statistics
• Deep understanding of current machine learning algorithms, under which circumstances each is applicable and their pros and cons.
• 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
• Good balance between Software and ML, being able to navigate both areas properly.
• Strong analytical and problem solving skills.
• Excellent written and verbal communication skills; mastery in English and local language. Ability to effectively communicate product architectures, design proposals and negotiate options at management levels.
• Passionate about learning new technologies and sharing knowledge with the team.
• Strong Stakeholder interaction skills, should be able to articulate and present the ML project details to stakeholders and leaders.

Desired qualifications include:

• Experience with recent ML Ops tools and solutions for development and deployment of ML solutions.
• Experience with time series forecasting with statistical and Machine Learning methods.
• Familiarity with agile development processes.
• Experience/familiarity with Agile tools such as JIRA or ADO

Education & Experience

• Bachelor's, Master's or PHD degree in Mathematics, Economics, Physics, Computer Science, or equivalent.
• Typically 6-10 years’ experience including graduate or postgraduate research.