Connected Vehicle Data Engineer

Jobtailor

Dearborn (MO)

On-site

USD 90,000 - 150,000

Full time

14 days+

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Job summary

Jobtailor is seeking a data science professional to apply machine learning to powertrain data, quantify risks, and perform inferential analytics to drive quality improvements. You will collaborate with Product Development and calibration teams, leveraging BigQuery SQL, PySpark, and in-vehicle calibration tools to validate model predictions against propulsion features.

Join a cross-functional team focused on extracting actionable insights from connected vehicle data and delivering data-driven

Qualifications

Bachelor’s degree in engineering, data science, CS, statistics, or related quantitative field.
3+ years of analytical, querying, and programming experience with SQL, Python, PySpark/Spark, and similar big data tools.
2+ years of experience in the automotive industry (Product Development, Calibration, and/or Quality).
Proven experience building and applying ML models to solve physical systems or engineering problems.

Responsibilities

Apply Machine Learning to Powertrain Data: Develop, train, and deploy ML models on curated powertrain data to detect anomalies, identify early-warning quality indicators, and predict component degradation.
Quantify & Assess Risk: Use statistical modeling and ML inference to quantify, assess, and prioritize risks associated with powertrain field quality issues, enabling data-driven decision-making.
Perform Inferential Analytics: Conduct inferential and diagnostic analytics to identify root causes of complex engineering and quality problems, translating CV big data into actionable insights.
Establish Stakeholder Alignment: Build strong working relationships with stakeholders in Product Development to ensure plans and requirements are understood and issues resolved.
Debug & Resolve Issues: Debug, root-cause, and resolve propulsion systems quality issues with cross-functional teams using connected vehicle data, ML models, and enterprise toolsets.
Foster Data Collection: Drive and optimize connected vehicle data collection strategies for solving engineering problems and characterizing customer usage patterns.
Query & Manipulate Big Data: Write highly proficient BigQuery SQL (and similar language) queries to extract, clean, and interpret massive connected vehicle datasets in the propulsion systems domain.
Develop Data Pipelines: Design, build, and own robust data pipelines and workflows using Python, PySpark, and modern data engineering tools to support ML model training and deployment.
Coordinate Data Creation: Partner with vehicle software teams to define and create new connected vehicle data elements, and support validation of telemetry signals.
Validate via Calibration Tools: Utilize in-vehicle calibration tools (ATI / ETAS) to collect high-frequency data to validate connected data and verify ML model predictions on key propulsion features.
Synthesize & Communicate Insights: Summarize and present ML models, statistical analyses, and big data findings in a visual way to technical and non-technical audiences, including executive leadership.

Skills

Machine Learning

Statistical Modeling

Inferential Analytics

Data Analysis

SQL

Python

PySpark

Data Engineering

Anomaly Detection

Regression

Education

Bachelor's Degree in Engineering, Data Science, Computer Science, Statistics, or related quantitative field

Tools

BigQuery

ATI Calibration Tools

ETAS Calibration Tools

Connected Vehicle Data

Enterprise Toolsets

Job description

Apply Machine Learning to Powertrain Data: Develop, train, and deploy machine learning models on curated powertrain data to detect anomalies, identify early-warning quality indicators, and predict component degradation.
Quantify & Assess Risk: Use statistical modeling and ML inference to quantify, assess, and prioritize risks associated with powertrain field quality issues, enabling data-driven decision-making.
Perform Inferential Analytics: Conduct inferential and diagnostic analytics to identify root causes of complex engineering and quality problems, translating CV big data into actionable insights.
Establish Stakeholder Alignment: Build strong working relationships with key stakeholders in Product Development to ensure that plans and requirements are fully understood, and issues are resolved effectively and efficiently.
Debug & Resolve Issues: Debug, root-cause, and resolve propulsion systems quality issues with cross-functional teams, leveraging connected vehicle data, ML models, and enterprise toolsets.
Foster Data Collection: Drive and optimize connected vehicle data collection strategies for solving engineering problems and characterizing customer usage patterns.
Query & Manipulate Big Data: Write highly proficient BigQuery SQL (and similar language) queries to extract, clean, and interpret massive, connected vehicle datasets in the propulsion systems domain.
Develop Data Pipelines: Design, build, and own robust data pipelines and workflows using Python, PySpark, and modern data engineering tools to support ML model training and deployment.
Coordinate Data Creation: Partner with vehicle software teams to define and create new connected vehicle data elements, and support the validation of these new telemetry signals.
Validate via Calibration Tools: Utilize in-vehicle calibration tools (ATI / ETAS) to collect high-frequency data to validate connected data and verify ML model predictions on key propulsion features and subsystems.
Synthesize & Communicate Insights: Summarize and present complex machine learning models, statistical analyses, and big data findings in a simplified, visual fashion to both technical and non-technical audiences, including executive leadership.

Requirements

Bachelor’s Degree in Engineering, Data Science, Computer Science, Statistics, or a related quantitative field.
3+ years of analytical, querying, and programming experience (SQL, Python, PySpark/Spark, and similar big data tools).
2+ years of experience in the automotive industry (Product Development, Calibration, and/or Quality).
Proven experience building and applying Machine Learning models (such as supervised/unsupervised learning, regression, classification, or anomaly detection) to solve physical systems or engineering problems.

Core Competencies

Demonstrates expertise in Machine Learning model development and deployment, particularly in the automotive sector, with a strong focus on data analysis, risk assessment, and stakeholder collaboration. Proficient in querying and manipulating big data to derive actionable insights and validate model predictions.

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