Reads like an Analyst role: dashboards and reporting.
Beijing, China
Actively hiring, first seen by us 1 day agoTurn individual field investigations into reusable analytical modules and dashboards
Connect vehicle, fleet, test and teardown data to validated features
Build screening modules and aging assessments for HV battery issues
Support root-cause and issue-scope investigations with battery experts
turning individual field investigations into reusable, scalable analytical capabilities
connect vehicle, fleet, test and teardown data and translate findings into validated features
reusable screening modules, dashboards and aging assessments
Embed these deliverables in reproducible workflows with battery experts and the RDE Data Roundtable
Minimum of 5 years of experience in the automotive industry or a comparable field
Degree in data science, computer science, statistics, electrical/automotive engineering or a related discipline
Strong working knowledge of Python and SQL for data analysis and modeling, time-series processing
Good understanding of and practical experience with HV battery technology and HV systems, including failure and aging mechanisms
Fluent written and spoken English
Practical experience with cloud data platforms or distributed processing of large time-series datasets is preferred.
Ability to use Spark or a comparable framework for large datasets is preferred. Experience with Azure Databricks, Parquet, Delta Lake, Git, experiment tracking and batch inference is an advantage. Knowledge of Xentry, Vector CANoe and DTS Monaco is a plus.
Actively hiringFirst seen by us 1 day ago, still listed, and not a repost of an earlier posting.
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Day 1
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- Minimum of 5 years of experience in the automotive industry or a comparable field, with a focus on battery development and/or data analytics/data science. - Hands-on experience in end-to-end telemetry analysis using Python/SQL, from raw-data, cohort and signal-quality checks through feature definition and packaging, evaluation, dashboards and reproducible engineering reporting. - Experience investigating vehicle or battery issues using individual-case evidence and fleet-level comparisons, correlating diagnostic, RTM, test and teardown data to develop testable hypotheses in collaboration with cross-functional specialists.
- Good understanding of and practical experience with HV battery technology and HV systems, including failure and aging mechanisms; ability to interpret cell voltage consistency, temperature distribution, SOC, current, charging behavior and operating conditions. - Strong working knowledge of Python and SQL for data analysis and modeling, time-series processing, data extraction/transformation, batch analysis, dashboards and reproducible reporting. Ability to define interpretable features and package calculation logic and thresholds into reusable modules for back-testing and screening validation. - Use approved AI-assisted development tools where appropriate, and independently review and verify generated code, analyses and technical conclusions.
- Ability to translate analytical findings, engineering evidence and limitations into clear recommendations for issue-resolution teams and technical stakeholders, and maintain reviewable, transferable code, data definitions and technical reports. - Ability to use Spark or a comparable framework for large datasets is preferred. Experience with Azure Databricks, Parquet, Delta Lake, Git, experiment tracking and batch inference is an advantage. Knowledge of Xentry, Vector CANoe and DTS Monaco is a plus. - Strong organizational, problem-solving and interpersonal skills; a results-oriented, responsible and adaptable team player.
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We file this job under Analyst, by the work the posting describes.
Analysts answer business questions with data. They pull numbers with SQL, build reports and dashboards, and explain what changed and why to the people making decisions.
Most postings ask for SQL, spreadsheets, a dashboard tool such as Tableau or Power BI, and clear writing.
Unlike a data scientist, an analyst mostly explains what happened rather than building models that predict what will.
Objectives:
Strengthen data-driven HV battery lifecycle management by turning individual field investigations into reusable, scalable analytical capabilities that support efficient issue resolution and evidence-based engineering decisions. As part of the Issue Resolution Team, connect vehicle, fleet, test and teardown data and translate findings into validated features, reusable screening modules, dashboards and aging assessments. Embed these deliverables in reproducible workflows with battery experts and the RDE Data Roundtable to support root-cause investigation, issue-scope and risk assessment, while making data limitations explicit and avoiding claims of assured failure prediction.
Qualification:
Education:
Degree in data science, computer science, statistics, electrical/automotive engineering or a related discipline.
Experience:
Minimum of 5 years of experience in the automotive industry or a comparable field, with a focus on battery development and/or data analytics/data science.
Hands-on experience in end-to-end telemetry analysis using Python/SQL, from raw-data, cohort and signal-quality checks through feature definition and packaging, evaluation, dashboards and reproducible engineering reporting.
Experience investigating vehicle or battery issues using individual-case evidence and fleet-level comparisons, correlating diagnostic, RTM, test and teardown data to develop testable hypotheses in collaboration with cross-functional specialists.
Practical experience with cloud data platforms or distributed processing of large time-series datasets is preferred.
Specific knowledge
Good understanding of and practical experience with HV battery technology and HV systems, including failure and aging mechanisms; ability to interpret cell voltage consistency, temperature distribution, SOC, current, charging behavior and operating conditions.
Strong working knowledge of Python and SQL for data analysis and modeling, time-series processing, data extraction/transformation, batch analysis, dashboards and reproducible reporting. Ability to define interpretable features and package calculation logic and thresholds into reusable modules for back-testing and screening validation.
Use approved AI-assisted development tools where appropriate, and independently review and verify generated code, analyses and technical conclusions.
Ability to assess missing data, sampling gaps, signal quality, cohort selection, data coverage, label quality, data leakage and model generalization in time-series analyses. Apply suitable baselines and independent validation; communicate uncertainty and distinguish screening rankings from failure prediction.
Ability to translate analytical findings, engineering evidence and limitations into clear recommendations for issue-resolution teams and technical stakeholders, and maintain reviewable, transferable code, data definitions and technical reports.
Ability to use Spark or a comparable framework for large datasets is preferred. Experience with Azure Databricks, Parquet, Delta Lake, Git, experiment tracking and batch inference is an advantage. Knowledge of Xentry, Vector CANoe and DTS Monaco is a plus.
Strong organizational, problem-solving and interpersonal skills; a results-oriented, responsible and adaptable team player.
Fluent written and spoken English; German language skills are beneficial.
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Beijing, China
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