Data Scientist IV

Panasonic•Irvine, CA

16h•Hybrid

About The Position

Leads analysis of complex, high-impact business problems using data from internal and external sources to provide insight to decisions at the leadership level. Works directly with aviation and in-flight entertainment data to identify opportunities specific to that domain. Identifies and interprets trends and patterns in datasets to locate influences. Have strong predictive, statistical modeling skills. Constructs forecasts, recommendations, and strategic/tactical plans based on business data and market knowledge. Creates specifications for reports and analysis based on business needs and required or available data elements. Provide consultation to users and lead cross-functional teams to address business issues. Directly produce datasets and reports for analysis using system reporting tools. Sets direction for modeling approaches and mentors other data scientists on the team.

Requirements

Strong hands-on Python and SQL skills for data science (pandas, scikit-learn, and similar).
Should be strong in AWS suite, experience working with AWS Cloud or similar environments and tools for Data Science, AI and ML
Ability to deploy ML models to production environment.
Able to take ambiguous data and turn it into a clear business case, and present that case persuasively to both technical and non-technical audiences.
Solid grounding in statistics: hypothesis testing, regression, experimental design (A/B testing).
Strong predictive, statistical modeling skills.
Strong ability to communicate both written and verbally to internal and external clients.
Ability to work independently with business users and cross functional leaders.
Ability to solve for higher ambiguity using data science.
Strong ability to create and deliver effective presentations to multiple audiences.
Advanced expertise using data analytic software such as R, Python, SQL.
Expert knowledge of Microsoft productivity tools: Excel, Word, and Visio and other software is necessary.
Ability to work in a collaborative environment and fast paced environment.
Ability to mentor junior data scientists and colleagues.

Nice To Haves

Sound knowledge in ETL and Data Warehouse concepts, experience with AWS cloud-based platforms.
Exposure to GenAI/LLM concepts (RAG, vector databases, prompt engineering) is a plus.
Knowledge of Extract Transform Load (ETL), Data Architecture preferred.
Masters in related field preferred
Open-source AI experience preferred.
Data Science Certifications Preferred.
Certified in AWS Cloud Preferred.
Experience in aviation, transportation or IoT/telemetry data domain is a plus

Responsibilities

Build, own, and defend predictive, classification, forecasting, and recommendation models using Python identifying emerging trends and business opportunities from customer, operational, and market data, while independently determining the modeling approach and technique for a given problem
Partner with Data Engineering on data infrastructure needs, translating modeling and analytics requirements into scalable data sources.
Employ the established Data Governance model including people, process, and technology to sustain Data Quality for the data objects and implement the necessary operating mechanisms to ensure compliance.
Build fault tolerant, self-healing, adaptive and highly accurate data computational and analytic processing applications.
Programing Develops and/or uses algorithms and ML prescriptive models and determines analytical approaches and modeling techniques to evaluate scenarios and potential future outcomes.
Applies analytical rigor and statistical methods to analyze large amounts of data, using advanced statistical techniques such as predictive statistical models, customer profiling, segmentation analysis, survey design and analysis and data mining.
Partner with business process experts and owners to build BI and leading indicators of operational benchmarks.
Mentor and guide the technical direction of junior data scientists, reviewing modeling approach and code to ensure rigor and consistency across the team
Partner with Data Engineering, Product, and Business stakeholders to help define what should be built, shaping the roadmap rather than just executing against it.