Data Scientist - Senior

Key Responsibilities:

Participates in projects to support key objectives and business goals through the use of data science methodology. Leverages data science methodology to solve complex business problems. Creates multiple algorithms using complex statistical methodologies through the use of statistical programming languages and tools. Partners with domain experts to verify model capabilities. Partners with Solution Architect to enable appropriate data flow/data model, development using appropriate tools/technology, rapid prototyping and informs the design of analytical products. Partners with less experienced employees on data science tools and methodologies. Clearly articulates results, methodologies and learnings to stakeholder and peer group. Continuous development and advancement of the team through knowledge sharing and collaboration.

Competencies:

Collaborates - Building partnerships and working collaboratively with others to meet shared objectives.

Customer focus - Building strong customer relationships and delivering customer-centric solutions.

Decision quality - Making good and timely decisions that keep the organization moving forward.

Manages complexity - Making sense of complex, high quantity, and sometimes contradictory information to effectively solve problems.

Tech savvy - Anticipating and adopting innovations in business-building digital and technology applications.

Data Mining - Extracts insights from data by identifying relationships and patterns through use of a suite of data exploration and data visualization techniques to understand the underlying structure of the data and enable sound conclusions upon model building.

Predictive Modeling - Develops analytical or machine learning models by using appropriate variable transformations, feature selection strategies, imputation strategies, class rebalancing, resampling strategies and quality control measures to generate predictive insights used in solving business questions.

Programming - Creates, writes and tests computer code, test scripts, and build scripts using algorithmic analysis and design, industry standards and tools, version control, and build and test automation to meet business, technical, security, governance and compliance requirements.

Requirements Analysis - Evaluates relationships and interdependencies between requirements based upon their complexity and value to the business in order to determine feasibility and prioritization.

Statistical Modeling - Develops descriptive and explanatory statistical models, and simulations for regression, classification, outlier detection, anomaly detection, time series forecasting using knowledge of foundational statistics such as null hypotheses significance tests, regression models, generalized linear modeling, time series analysis, rank statistics, probability distribution fitting survival analysis, etc. to validate hypotheses for any given statistical or business question.

Problem Solving - Solves problems and may mentor others on effective problem solving by using a systematic analysis process by leveraging industry standard methodologies to create problem traceability and protect the customer; determines the assignable cause; implements robust, data-based solutions; identifies the systemic root causes and ensures actions to prevent problem reoccurrence are implemented.

Values differences - Recognizing the value that different perspectives and cultures bring to an organization.