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. May provide mentoring and guidance to lower level employees.
What a Expert Data Scientist does in HP.
- 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
Individuals who do well in this role at HP, usually possess:
Education and Experience Required
- 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.
Knowledge and Skills
- 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.
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