Senior Specialist, Data Science

CPKC•Kansas City, MO

6d•Onsite

About The Position

Develop and implement advanced statistical and machine learning models to analyze large-scale operational datasets and generate predictive insights related to performance, reliability, and efficiency. Design and build scalable data pipelines and feature engineering processes to support model development, training, and deployment. Perform exploratory data analysis and apply statistical techniques to identify patterns, trends, and relationships across complex datasets. Develop predictive models for use cases such as demand forecasting, anomaly detection, predictive maintenance, and operational optimization. Evaluate model performance using appropriate metrics and refine models to improve accuracy, robustness, and generalizability. Integrate machine learning models into production environments, collaborating with engineering teams to support deployment and monitoring. Analyze large and complex datasets using programming languages such as Python and SQL, and leverage libraries for statistical analysis and machine learning. Design experiments and conduct hypothesis testing to support data-driven decision-making and validate business assumptions. Translate business problems into analytical frameworks and communicate findings, insights, and recommendations to technical and non-technical stakeholders. Develop and maintain documentation for data models, methodologies, and analytical processes to support reproducibility and governance. Collaborate with cross-functional teams to identify opportunities for applying advanced analytics and to align modeling approaches with business objectives. Create data visualizations and analytical reports to communicate model outputs, trends, and key performance indicators.

Requirements

Bachelor’s degree or its U.S. equivalent in Computer Science, Information Systems, Information Technology, Computer Engineering, or a related field, plus 5 years of professional experience as a Software Engineer, Data Analyst, or any occupation, job title, position involving conducting data analysis, data manipulation, automation and statistical analysis.
Master’s degree or its U.S. equivalent in Computer Science, Information Systems, Information Technology, Computer Engineering, or a related field, plus 3 years of professional experience as a Software Engineer, Data Analyst, or any occupation, job title, position involving conducting data analysis, data manipulation, automation and statistical analysis.
3 years of professional experience conducting data analysis using SQL, including data profiling, data validation, and extracting actionable insights from large data sets
3 years of professional experience writing, optimizing, and troubleshooting advanced SQL queries, including complex joins, aggregations, subqueries, stored procedures, functions, and performance tuning in relational databases
3 years of professional experience performing data manipulation, automation, and statistical analysis, including building and maintaining data pipelines and ETL processes to integrate data from multiple automotive-related data sources
3 years of professional experience applying statistical analysis and predictive modeling techniques, including regression analysis, time-series forecasting, and machine learning, to operations data for insights including predictive maintenance and capacity forecasting
3 years of professional experience documenting SQL queries, logic, data workflows, and analytics results for technical and non-technical stakeholders
3 years of professional experience collaborating with cross-functional teams, including business, IT, and finance, to gather requirements, clarify business needs, and deliver solutions strictly using SQL

Responsibilities

Develop and implement advanced statistical and machine learning models to analyze large-scale operational datasets and generate predictive insights related to performance, reliability, and efficiency
Design and build scalable data pipelines and feature engineering processes to support model development, training, and deployment
Perform exploratory data analysis and apply statistical techniques to identify patterns, trends, and relationships across complex datasets
Develop predictive models for use cases such as demand forecasting, anomaly detection, predictive maintenance, and operational optimization
Evaluate model performance using appropriate metrics and refine models to improve accuracy, robustness, and generalizability
Integrate machine learning models into production environments, collaborating with engineering teams to support deployment and monitoring
Analyze large and complex datasets using programming languages such as Python and SQL, and leverage libraries for statistical analysis and machine learning
Design experiments and conduct hypothesis testing to support data-driven decision-making and validate business assumptions
Translate business problems into analytical frameworks and communicate findings, insights, and recommendations to technical and non-technical stakeholders
Develop and maintain documentation for data models, methodologies, and analytical processes to support reproducibility and governance
Collaborate with cross-functional teams to identify opportunities for applying advanced analytics and to align modeling approaches with business objectives
Create data visualizations and analytical reports to communicate model outputs, trends, and key performance indicators