Senior Data Scientist - Insights and Analytics

Hardware Engineering is seeking a Senior Data Scientist to operate at the intersection of data engineering and business intelligence - building the scalable infrastructure that powers data-driven decisions while delivering the analytics and insights that drive strategic direction. The ideal candidate brings equal expertise in data pipeline engineering and analytics, with a passion for both architecting robust data systems and translating complex outputs into compelling narratives. You'll have the autonomy to shape both the engineering foundation and analytics strategy across workforce planning and operations, working with leadership to inform high-stakes organizational decisions. This role offers the opportunity to own the full data lifecycle while building a portfolio of high-impact projects spanning infrastructure and insight.

Description

You'll operate across the full data stack - designing and building the infrastructure that enables insight, then leveraging that infrastructure to answer critical business questions. Projects will span pipeline development, data modeling, workforce planning, operational analytics, and strategic initiatives across Hardware Engineering.

This role requires collaboration within a multi-disciplined, geographically distributed data science team, owning both the engineering foundation and the analytics layer built upon it, while working closely with business stakeholders and platform teams to shape the end-to-end data lifecycle.business analytics projects through all phases - defining investigations, exploring data, conducting analysis, and presenting results to business customers. Projects will span workforce planning, operational analytics, and strategic initiatives across Hardware Engineering.

Responsibilities:

Design, build, and maintain scalable data pipelines (batch and streaming) and data warehouse models that serve as the analytical foundation for Hardware Engineering

Build repeatable analytical frameworks and dashboards that become the standard for how Hardware Engineering evaluates organizational health and plans for growth

Own the full data lifecycle - from pipeline design and data modeling through to insight delivery - ensuring data quality, reliability, and accessibility across the organization

Inform strategic decisions across the HWE organization, from workforce planning to operational efficiency, through presentations, visual dashboards, and reports

Build forecasting and optimization models for engineering resource needs, translating complex technical constraints into strategic recommendations for senior stakeholders

Define and implement data models, schemas, and transformation logic that bridge infrastructure capabilities with evolving business needs

Preferred Qualifications

MS/MA in Computer Science, Software Engineering, Data Science, or equivalent degree

Experience with dbt, Apache Spark, or similar data transformation and processing frameworks

Experience collaborating with or leading cross-functional teams on pipeline design, data quality frameworks, and monitoring solutions

Experience with prompt engineering and leveraging LLMs for data analysis, automation, or insight generation workflows

Proficiency in JavaScript for data visualization, web-based dashboard development, or lightweight front-end tooling (e.g., D3.js, Observable)

Minimum Qualifications

Minimum BS/BA in Computer Science, Software Engineering, Data Science, or equivalent degree

5+ years defining and leading business analytics initiatives, including surfacing insights, explaining outliers, building forecasting algorithms, and effectively communicating findings to stakeholders at all levels, including senior leadership

5+ years of experience designing and building data pipelines (batch and streaming), data modeling, and data warehousing in cloud-based platforms like AWS or Snowflake

Demonstrable mastery of Python across both data engineering (pipeline development, orchestration) and data analysis, including proficiency in pandas, NumPy, scikit-learn, and data visualization libraries for stakeholder reporting

Self-directed problem-solver comfortable working through ambiguity, managing multiple priorities, and driving projects from definition through delivery

Hands-on experience with cloud data platforms (AWS, Snowflake) and pipeline orchestration tools (e.g., Airflow, dbt)