Specialist, Data Scientist

Quick Facts

Role: Data Scientist (VALUE)

Level: IC20 Emerging Specialist

Focus: Data-to-insights analytics and AI-enabled capabilities (including Generative AI, LLMs, and RAG)

Description

As a Data Scientist on the VALUE team, you will transform data into actionable insights that drive product strategy, operational excellence, and customer outcomes. You will analyze structured and unstructured data, develop statistical and machine learning solutions, and communicate findings through clear visualizations, reporting, and recommendations.

You will partner with Product Managers, Software Engineers, Business Analysts, Data Engineers, and Quality Engineers to identify opportunities where data and AI can improve decision-making, automate workflows, enhance customer experiences, and create measurable business value.

This role contributes to the organization’s use of Artificial Intelligence technologies, including Generative AI, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) solutions, with responsible, secure, and measurable AI use.

Responsibilities

AI & Emerging Technology Contributions (40%)

• Contribute to AI-enabled products and operational initiatives across the VALUE portfolio.
• Support experimentation and implementation of Generative AI, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG).
• Assist in the development and evaluation of prompts, knowledge retrieval strategies, model outputs, and AI-assisted workflows.
• Build and monitor evaluation frameworks measuring AI accuracy, relevance, reliability, latency, and business impact.
• Partner with engineering teams to integrate AI capabilities into production-ready services and platforms.
• Help establish best practices for responsible AI, model monitoring, governance, transparency, and human oversight.

Statistical Modeling & Machine Learning (30%)

• Develop, validate, and maintain statistical and machine learning models that support VALUE business objectives.
• Apply predictive analytics, classification, forecasting, clustering, recommendation, and optimization techniques where appropriate.
• Evaluate model performance and continuously refine solutions using measurable outcomes and stakeholder feedback.
• Ensure model quality through testing, validation, documentation, and performance monitoring.

Collaboration (20%)

• Partner with Product Managers and Business Analysts to translate business questions into analytical solutions.
• Collaborate across Engineering, Product, Architecture, and Operations teams to maximize data-driven decision making.
• Effectively communicate technical findings, assumptions, risks, and recommendations to diverse audiences.
• Share knowledge and mentor peers through collaboration, documentation, and technical discussions.

Data Analysis & Insights (10%)

• Analyze large, complex datasets to identify trends, patterns, risks, and opportunities.
• Transform raw data into actionable recommendations that support business and product decisions.
• Develop dashboards, visualizations, reports, and analytical models that communicate effectively to technical and non-technical stakeholders.
• Define metrics, KPIs, and measurement frameworks to evaluate product and business performance.
• Perform exploratory analysis and hypothesis testing to validate assumptions and inform strategic decisions.

Benefits

• Salary range: $125,000 – $140,000.
• Eligible to participate in an annual incentive program.
• Information on benefits is available via the provided benefits link.

Requirements

• Bachelor’s degree in data science, Statistics, Mathematics, Computer Science, Engineering, Analytics, or related field, or equivalent practical experience.
• 3+ years of experience in data science, advanced analytics, machine learning, or related analytical roles.
• Demonstrated experience using Python for data analysis, modeling, and automation.
• Experience with statistical analysis, exploratory data analysis, and predictive modeling.
• Experience working within Agile product or engineering teams.
• Experience with cloud-based analytics platforms (Azure preferred).
• Knowledge of data quality, governance, and responsible AI practices.