Senior Data Analyst

IONNA LLC•Raleigh-Durham, NC, NC

3d•Onsite

About The Position

We are seeking a highly skilled Senior Data Analyst with a strong foundation in data science, statistical modeling, and advanced analytics and AI platforms to drive data-informed decision-making across the organization. This role sits at the intersection of AI, analytics, data science, and business strategy—transforming complex data into actionable insights, predictive solutions, and measurable outcomes. The ideal candidate is comfortable working with large, complex datasets, building advanced analytical models, and communicating findings clearly to both technical and non-technical stakeholders.

Requirements

Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, Economics, or a related quantitative field (Master’s degree preferred).
A minimum of 5 years of experience in data analytics, data science, or a related analytical role.
Strong knowledge of statistical analysis, modeling techniques, and data science methodologies.
Experience working with large datasets and modern data platforms (e.g., cloud data warehouses, distributed systems).
Proven ability to translate data insights into business impact.
Excellent communication and stakeholder management skills.

Nice To Haves

Experience deploying or operationalizing machine learning models in a production environment.
Familiarity with A/B testing frameworks and experimental design.
Experience with cloud platforms (e.g., AWS, Azure, GCP) and modern analytics stacks.
Knowledge of data governance, data quality, and privacy best practices.
Prior experience in EV Charging, consumer behavior, technical quality analytics.

Responsibilities

Analyze large, complex, and multi-source datasets to uncover trends, patterns, and actionable insights.
Translate analytical findings into clear recommendations that influence product strategy, operations, and executive decision-making.
Develop data-driven forecasts, simulations, and scenario analyses to support strategic planning.
Apply statistical techniques (e.g., regression, hypothesis testing, time-series analysis, clustering) to solve complex business problems.
Develop AI models and datasets to inform business decisions, correlate data and drive decision-making.
Write efficient, well-documented SQL and/or Python/R code for data extraction, transformation, and analysis.
Partner with data engineering teams to ensure high-quality, well-structured, and reliable datasets.
Maintain internal utilization models and support continual enhancement and development of utilization models.
Act as a thought leader and mentor for team members and business partners.
Collaborate closely with stakeholders across product, finance, operations, marketing, and quality.
Help define best practices for analytics, experimentation, and data science across the organization.