Senior Data Scientist, Azure ML Data Platform

Molex; Lisle (IL); On-site; USD 160,000 - 200,000; Full time

Benefits offered by this job

Medical, Dental, Vision

HSA/FSA

Life Insurance

Disability

Retirement Plan

Paid Vacation

Educational Assistance

Parental Leave

Job summary

Molex is seeking a Data Scientist to own the data backbone of an Azure-based AI/ML platform. You will transform historical design and simulation results into clean, structured datasets and statistically validate models before they inform design decisions.

You will build ETL pipelines, run analyses, and create Power BI dashboards to monitor data and model health, applying DOE to sample the design space efficiently and effectively.

Qualifications

• Hands-on experience building and validating AI/ML models - not just data prep or descriptive analytics.
• 10+ years in a data science role, ideally with engineering, simulation, or scientific data.
• Strong Python (pandas, NumPy, scikit-learn) and SQL.
• Comfortable with Azure data tools (Azure Data Factory, Databricks, Synapse) or similar cloud stack.
• Solid statistics grounding: hypothesis testing, regression, DOE, variance/sensitivity analysis.
• Experience with data visualization/dashboarding tools (Power BI or similar).

Responsibilities

• Build and maintain ETL pipelines (Azure Data Factory / Azure Databricks) that structure design parameters and simulation results for ML use.
• Run statistical and exploratory analysis to identify which design parameters drive performance outcomes.
• Validate model outputs against ground-truth simulation results and flag data quality issues.
• Track model and pipeline performance over time; build Power BI dashboards for data and model health visibility.
• Apply DOE principles to sample the design space efficiently.

Skills

Python (pandas, NumPy, scikit-learn)

SQL

Statistics

Power BI

Tools

Azure Data Factory

Azure Databricks

Azure Synapse

Power BI

Job description

Molex is seeking a Data Scientist to own the data backbone of an Azure-based AI/ML platform. You will transform historical design and simulation results into clean, structured datasets and statistically validate models before they inform design decisions.

You will build ETL pipelines, run analyses, and create Power BI dashboards to monitor data and model health, applying DOE to sample the design space efficiently and effectively.