layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

Data Scientist - Product Safety Data Analytics at General Motors | LAYIQ

Develop and help standardize best practices for continuous monitoring of emerging safety issues for use in safety hazard monitoring.
Strengthen data-analytics support for safety investigations through improved methods, automated tools, and feedback loops for process improvement.
Analyze signals, patterns, and trends in large and complex data sets to support early identification of potential safety issues.
Support anomaly detection, diagnostics, prognostics, and root cause analysis for safety-related topics.
Design, build, and maintain repeatable analytical workflows using Python, SQL/Spark, Databricks, and related tools.
Identify automation opportunities and implement solutions using custom software, reusable code, data pipelines, and appropriate off-the-shelf technologies.
Prepare clear visualizations, summaries, and presentations that synthesize complex data and concepts for technical and non-technical audiences, including senior leadership.
Communicate assumptions, methods, limitations, and findings clearly to support sound decision-making.
Build knowledge of vehicle safety technologies, their design intent and function, and their intended performance in the field.
Your Skills & Abilities (Required Qualifications): 3+ years of work experience in applied statistics, analytics, engineering, data science, or a related field.
Bachelor’s degree in Applied Statistics, Mathematics, Econometrics, Computer Science, Operations Research, or a related quantitative discipline.
Proficiency in statistical data analysis, including methods such as outlier detection, time series analysis, categorical data analysis, multivariate analysis, sampling design, or analysis of variance.
Experience analyzing structured and/or unstructured data, including text or other sources that require data-mining techniques.
Proficiency in Python and SQL/Spark, with the ability to develop clear, reusable, and maintainable analytical code.
Knowledge of statistical graphics and experience with visualization tools such as Power BI and Tableau.
Experience working with modern data environments such as Databricks, data pipelines, data preprocessing workflows, and automation of recurring analytical processes.
Ability to communicate results and methodologies effectively, including presentation to senior leadership.
Strong work ethic, drive for results, and ability to work effectively in an ambiguous environment.
Understanding of vehicle safety technologies including design intent, function, and intended field performance.

Data Scientist - Product Safety Data Analytics

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