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Data Scientist (Automotive Logistics, AWS Cloud)

InnoCore Solutions, Inc.

Dallas, Texas • Yesterday

Easy Apply

Contract

Depends on Experience

Job Details

Skills

Python
SQL
Machine Learning
AWS
Demand Forecasting
AWS SageMaker
MLOps
GitHub
Linear Programming (LP)
Dynamic Programming (DP)

Summary

We are hiring a Lead Data Scientist to drive the architecture, development, and deployment of machine learning and AI-powered demand forecasting solutions within the automotive logistics ecosystem. This role is central to improving vehicle and parts supply chain visibility, inventory accuracy, and fulfillment predictability using Linear Programming (LP), Dynamic Programming (DP), machine learning, MLOps best practices, and AWS-native services.

Responsibilities:

Develop and implement optimization models using Linear Programming (LP), Mixed Integer Linear Programming (MILP), and Dynamic Programming (DP) to solve complex supply chain problems, including inventory optimization, production planning, transportation, and network optimization.
Build scalable optimization solutions using Python and optimization libraries such as Pyomo, PuLP, OR-Tools, Gurobi, or CPLEX, and deploy them on cloud platforms.
Collaborate with supply chain, operations, and business stakeholders to translate complex business requirements into mathematical optimization models and decision-support solutions.
Continuously monitor model accuracy and improve forecasts based on error analysis, drift detection, and business input.
Align modeling strategy with supply chain KPIs such as fill rate, inventory turnover, order-to-ship lead time, and forecast bias.
Develop and manage end-to-end ML pipelines using Amazon SageMaker Pipelines, AWS Step Functions, and CodePipeline.
Automate model training, testing, deployment, monitoring, and rollback using CI/CD practices tailored for ML.
Implement SageMaker Model Monitor, SageMaker Clarify, and CloudWatch for continuous model performance, bias, and drift monitoring.
Use AWS Lambda and EventBridge to integrate real-time triggers for retraining or alerts.
Define and operate a SageMaker Feature Store for training and inference consistency.
Develop AI-driven decision support tools using classification models, clustering, anomaly detection, and explainable AI (XAI).
Explore use of Generative AI for scenario simulation, forecast explanation, and automated reporting.
Collaborate with business teams to embed AI recommendations into dashboards, alerts, or APIs.
Act as a bridge between technical teams and business stakeholders (supply chain, logistics, planning).
Promote best practices in model documentation, reproducibility, testing, and governance.

Requirements:

Experience developing optimization models using Linear Programming (LP), Mixed Integer Linear Programming (MILP), Dynamic Programming (DP), or other Operations Research techniques.
Proficiency in Python and experience with optimization frameworks such as Pyomo, PuLP, Google OR-Tools, Gurobi, or IBM CPLEX.
At least 2 years of experience in applying statistical and machine learning techniques to real-world problems.
Solid understanding of forecasting techniques, statistical modeling, and time series analysis.
Knowledge of methods like Logistic Regression, Time Series Analysis, GLMs, Mixed Modeling, Multivariate Statistics, Predictive Modeling, Decision Trees, Gradient-Boosted Trees, Random Forests, and Neural Networks.
Hands-on experience with AWS services: Amazon SageMaker, S3, Glue, Lambda, CloudWatch, Step Functions, ECR, CodePipeline.
Strong SQL skills and familiarity with data lakes, Redshift/Snowflake, and distributed data processing (Spark).
Experience implementing MLOps pipelines in production environments.
Deep understanding of automotive logistics, including order lifecycle, dealer distribution, parts inventory, and transportation flows.
Experience with version control systems such as GitHub, and familiarity with CI/CD practices to streamline model deployment and code management.
Prior experience with demand forecasting or supply chain analytics at scale.

Education:

Advanced degree (MS or PhD) in a quantitative field including but not limited to Statistics, Computer Science/Data Science, Operations Research, Industrial Engineering, or Applied Mathematics.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 10120357
Position Id: DS-0926
Posted 30+ days ago

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Grand Prairie, Texas

•

3d ago

Role: Data Scientist Location: Grand-Prairie, Texas 75050 Work Model: Onsite (4 days a week work from office Mon - Thu) Duration: Fulltime Employee Note: Relocation assistance will be provided by Client. As part of our commitment to our employees, Client provides a robust benefits package for full-time employees, which includes: Medical Coverage, Dental and Vision (100% Client contribution for the employee, 80% contribution for immediate dependents). 15 days of Paid Time Off (PTO). 10 pai

Full-time, Contract, Third Party

Python AWS AI Architect

Richardson, Texas

•

4d ago

Required Skill and Experience Experience in Python and ML libraries (e.g., TensorFlow, PyTorch, scikit-learn). Experience in Design, develop, and fine-tune machine learning models, particularly those involving LLMs and generative AI. Experience with AWS services, especially Bedrock, S3, EC2, and Lambda Familiarity with ML Ops practices and tools for model deployment and monitoring. Experience with AWS SageMaker for model development and model deployment. Experience with developing and deploying

Easy Apply

Full-time

$140,000 - $160,000

Python AWS AI Architect/Engineer

Richardson, Texas

•

5d ago

Job Description: Job Title:Python AWS AI Architect/Engineer Location:Hartford, CT, Raleigh, NC, Richardson, TX, Tempe, AZ Duration: Full time Required Skill and Experience Experience in Python and ML libraries (e.g., TensorFlow, PyTorch, scikit-learn). Experience in Design, develop, and fine-tune machine learning models, particularly those involving LLMs and generative AI. Experience with AWS services, especially Bedrock, S3, EC2, and Lambda Familiarity with ML Ops practices and too

Easy Apply

Full-time, Third Party

$120000 - $150000

AWS SageMaker Engineer/ MLOps Platform Engineer

Plano, Texas

•

Today

Job Title: MLOps Platform Engineer (SageMaker) Location: Plano, TX (Onsite) Duration: 12 Months Description: RM Notes: Export Control form will be required during onboarding only and is not required at the time of submission. This position is with the Enterprise Analytical Data & Integration Team. The hiring manager is looking to onboard an experienced MLOps Platform Engineer with strong expertise in AWS and Amazon SageMaker. Local candidates are preferred. 12-month contract with possible exte

Easy Apply

Contract

Job Details

Skills

Python
SQL
Machine Learning
AWS
Demand Forecasting
AWS SageMaker
MLOps
GitHub
Linear Programming (LP)
Dynamic Programming (DP)

Summary

Responsibilities:

Develop and implement optimization models using Linear Programming (LP), Mixed Integer Linear Programming (MILP), and Dynamic Programming (DP) to solve complex supply chain problems, including inventory optimization, production planning, transportation, and network optimization.
Build scalable optimization solutions using Python and optimization libraries such as Pyomo, PuLP, OR-Tools, Gurobi, or CPLEX, and deploy them on cloud platforms.
Collaborate with supply chain, operations, and business stakeholders to translate complex business requirements into mathematical optimization models and decision-support solutions.
Continuously monitor model accuracy and improve forecasts based on error analysis, drift detection, and business input.
Align modeling strategy with supply chain KPIs such as fill rate, inventory turnover, order-to-ship lead time, and forecast bias.
Develop and manage end-to-end ML pipelines using Amazon SageMaker Pipelines, AWS Step Functions, and CodePipeline.
Automate model training, testing, deployment, monitoring, and rollback using CI/CD practices tailored for ML.
Implement SageMaker Model Monitor, SageMaker Clarify, and CloudWatch for continuous model performance, bias, and drift monitoring.
Use AWS Lambda and EventBridge to integrate real-time triggers for retraining or alerts.
Define and operate a SageMaker Feature Store for training and inference consistency.
Develop AI-driven decision support tools using classification models, clustering, anomaly detection, and explainable AI (XAI).
Explore use of Generative AI for scenario simulation, forecast explanation, and automated reporting.
Collaborate with business teams to embed AI recommendations into dashboards, alerts, or APIs.
Act as a bridge between technical teams and business stakeholders (supply chain, logistics, planning).
Promote best practices in model documentation, reproducibility, testing, and governance.

Requirements:

Experience developing optimization models using Linear Programming (LP), Mixed Integer Linear Programming (MILP), Dynamic Programming (DP), or other Operations Research techniques.
Proficiency in Python and experience with optimization frameworks such as Pyomo, PuLP, Google OR-Tools, Gurobi, or IBM CPLEX.
At least 2 years of experience in applying statistical and machine learning techniques to real-world problems.
Solid understanding of forecasting techniques, statistical modeling, and time series analysis.
Knowledge of methods like Logistic Regression, Time Series Analysis, GLMs, Mixed Modeling, Multivariate Statistics, Predictive Modeling, Decision Trees, Gradient-Boosted Trees, Random Forests, and Neural Networks.
Hands-on experience with AWS services: Amazon SageMaker, S3, Glue, Lambda, CloudWatch, Step Functions, ECR, CodePipeline.
Strong SQL skills and familiarity with data lakes, Redshift/Snowflake, and distributed data processing (Spark).
Experience implementing MLOps pipelines in production environments.
Deep understanding of automotive logistics, including order lifecycle, dealer distribution, parts inventory, and transportation flows.
Experience with version control systems such as GitHub, and familiarity with CI/CD practices to streamline model deployment and code management.
Prior experience with demand forecasting or supply chain analytics at scale.

Education:

Advanced degree (MS or PhD) in a quantitative field including but not limited to Statistics, Computer Science/Data Science, Operations Research, Industrial Engineering, or Applied Mathematics.

Dice Id: 10120357
Position Id: DS-0926
Posted 30+ days ago

Fitment

Dice Job Match Score™

To see how well you match this job, please log in or create an account.

Once logged in, be sure to complete your profile to get the most accurate match score.

Create job alert

Never miss an opportunity! Create an alert based on the job you applied for.

Grand Prairie, Texas

•

3d ago

Full-time, Contract, Third Party

Python AWS AI Architect

Richardson, Texas

•

4d ago

Easy Apply

Full-time

$140,000 - $160,000

Python AWS AI Architect/Engineer

Richardson, Texas

•

5d ago

Easy Apply

Full-time, Third Party

$120000 - $150000

AWS SageMaker Engineer/ MLOps Platform Engineer

Plano, Texas

•

Today

Easy Apply

Contract

Data Scientist (Automotive Logistics, AWS Cloud)

Skills

Summary

Dice Job Match Score™

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Data Scientist (Automotive Logistics, AWS Cloud)

Job Details

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Summary

Dice Job Match Score™

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