Data Scientist

Applies advanced subject matter knowledge to solve complex business issues and is regarded as a subject matter expert. Frequently contributes to the development of new ideas and methods. Works on complex problems where analysis of situations or data requires an in-depth evaluation of multiple factors. Leads and/or provides expertise to functional project teams and may participate in cross-functional initiatives. Acts as an expert providing direction and guidance to process improvements and establishing policies. Frequently represents the organization to external customers/clients. Exercises significant independent judgment within broadly defined policies and practices to determine best method for accomplishing work and achieving objectives.

Learn more about HP's global Management levels.

• Mines data using modern tools and programming languages
• Apply advanced statistical and predictive modeling techniques to build, maintain, and improve decision systems
• Develop innovative and effective approaches to solve analytics problems and communicate results and methodologies
• Defines and implements models to uncover patterns and predictions creating business value and innovation
• Ability to translate business needs to technical requirements, propose and validate solutions
• Manages relationships with business partners to evaluate and foster data driven innovation, provide domain-specific expertise in cross-organization projects/initiatives
• Ties insights into effective visualizations communicating business value and innovation potential.
• Maintains proficiency within the data science domain by keeping up with technology and trend shifts. Contributes to industry data science domain initiatives
• Represents the data science team for all phases of larger and more-complex development projects
• Leads project team(s) of data science professionals, assuring insights are communicated regularly and effectively, reviewing designs, models and accuracy and data compliance.
• Drives the implementation of agreed improvement actions, initiatives, projects agreed with business and management
• Executes and coordinate internal customer requirements management and change management processes
• Proposes corrective action plans to business, management and stakeholders
• Supports quality improvement projects aiming at improving operational excellence and cost efficiency
• Creates a working environment that is conducive to individual growth, high performance, is challenging and rewarding
• Provides guidance, training and mentoring to less experienced staff members

Classification Guidance
The sections below help differentiate between levels to enable consistency.

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

• Extensive experience using statistics, mathematics, algorithms and programming languages to solve big data challenges
• Fluent in structured and unstructured data, its management, and modern data transformation methodologies
• Ability to define and create complex models to pull valuable insights, predictions and innovation from data
• Effectively and creatively tell stories and create visualizations to describe and communicate data insights
• Python, Numpy, Pandas, Matplot, Sci Kit learn, One or more Deep learning libraries (Keras, Pytorch, Tensorflow etc.)
• Machine Learning algorithms (Decision Trees, Random Forest, Gradient boosting, Naïve Bayes, PCA etc.)
• Deep Learning architectures (ResNet, Inception, VGGNet, CNN, RNN etc.)
• Math (Matrices, Calculus, Linear Algebra, Probability)
• Statistical Techniques (Linear/Logistic regression, Hypothesis testing, forecasting etc.)
• Advanced knowledge of Excel and Access preferred (Arrays, VBA etc.)
• Knowledge of SQL preferred
• Excellent written and verbal communication skills; mastery in English and local language
• Ability to effectively communicate data insights and negotiate options at senior management levels.

• Collaborates with peers, junior engineers, data scientists and project team.
• Typically interacts with high- level Individual Contributors, Managers, Directors and Program Core Teams.
• Leads multiple projects requiring data engineering solutions development.
• Drives design innovation.

• Moderate to High