Data Scientist

Aquent, a leader in connecting top talent with the world's most innovative companies, is partnering with a prominent industrial organization, offering an unparalleled opportunity to drive significant impact. This role empowers you to transform complex operational data into actionable insights that directly optimize production processes, enhance quality, and minimize downtime across dynamic manufacturing environments. Your expertise will be instrumental in building robust models that stand up to real-world plant challenges, making a tangible difference in efficiency and output and shaping the future of industrial efficiency.

About Our Partner

Our partner is a prominent industrial organization at the forefront of manufacturing excellence, pioneering advancements in how goods are produced and delivered. They are dedicated to leveraging cutting-edge technology to optimize operations, enhance product quality, and drive efficiency across their dynamic environments. Joining this team means contributing to a future where data-driven decisions power industrial innovation.

About the Opportunity

We are seeking a highly skilled and experienced professional to join a pioneering team focused on elevating manufacturing operations through advanced data science. This is a hands-on role where you will dive deep into factory and industrial data, identifying potential issues before they arise, validating models against the realities of the plant floor, and empowering operational teams with data-driven solutions. You will be the crucial link between raw data and real-world impact, contributing directly to significant improvements in production and operational efficiency.

What You'll Do

• Frame complex manufacturing problems collaboratively with plant and engineering partners, challenging assumptions on labels, ground truth, and "accuracy" expectations to ensure realistic goals.
• Explore, clean, and seamlessly integrate fragmented operational data from diverse sources, including machines, sensors, quality systems, maintenance logs, production records, and MES/historian extracts.
• Develop and rigorously validate statistical and machine-learning models for anomaly detection, quality prediction, equipment health monitoring, and process optimization, emphasizing business cost and false positive/negative rates over singular leaderboard metrics.
• Translate complex model outputs into practical, usable insights for engineers and operators, providing clear thresholds, explanations, and actionable guidance ("what to do when this fires"), and offering ongoing support for models in production.
• Collaborate effectively with data engineering and software teams on pipelines, Databricks, and production deployments, acting as the key customer for platform capabilities rather than building them yourself.

What We're Looking For

We are seeking a pragmatic problem-solver who thrives on bringing data science to life in operational settings. You should be able to articulate a complete journey from raw data to real-world impact: describing the data's nuances, detailing your findings, presenting your model, explaining how you validated its accuracy (or identified its flaws), and demonstrating the tangible actions operations took based on your work.

• Proven experience in addressing typical industrial challenges such as process drift, abnormal machine behavior, quality prediction, equipment health, bottlenecks, downtime, scrap/rework, and root-cause analysis.
• Proficiency in applying a diverse range of analytical methods tailored to the problem, including statistical limits, clustering, isolation forest, time series analysis, autoencoders, and supervised models when labeled data is available.
• A strong ability to explore, clean, and join messy, real-world sensor or process data, defining "normal" versus "abnormal" behavior in collaboration with operational experts.
• Experience in validating models against real outcomes and clearly communicating the limitations of models when data constraints exist.

Bonus Points If You Have

• Direct experience in manufacturing environments.
• Backgrounds in industrial IoT, equipment, quality, automotive, semiconductor, energy, telecom/operations, or similar operational settings where you have applied data science to complex sensor or process data.

This Role Is Not For You If Your Recent Work Primarily Involves

• Data engineering or data modeling (e.g., lakehouse architectures, medallion patterns, star schemas, Unity Catalog, ADF, or building "pipelines for the DS team").
• MLOps or ML platform engineering (e.g., Airflow, SageMaker plumbing, FastAPI services, CI/CD) with minimal actual data analysis.
• Generative AI product development (e.g., RAG chatbots, LangChain agents, Copilot apps) as your primary focus.
• BI and reporting (e.g., Power BI/Tableau KPI dashboards) without significant model development.
• Resumes that list industry terms like MES, SCADA, PLC, JPH, Databricks, and anomaly detection but lack specific examples of datasets analyzed, findings discovered, or validation results achieved.

About Aquent Talent:

Aquent Talent connects the best talent in marketing, creative, and design with the world's biggest brands.

Our eligible talent get access to amazing benefits like subsidized health, vision, and dental plans, paid sick leave, and retirement plans with a match. We also offer free online training through Aquent Gymnasium. More information on our awesome benefits!

Aquent is an equal-opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, and other legally protected characteristics. We're about creating an inclusive environment-one where different backgrounds, experiences, and perspectives are valued, and everyone can contribute, grow their careers, and thrive.