Search Jobs | Dice.com

1 results (0 new)

Group by company

Data Scientist (Automotive Logistics, AWS Cloud)

InnoCore Solutions, Inc.

Dallas, Texas • Today

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-0626
Posted 30+ days ago

Fitment

Dice Job Match Score™

🎯 Assessing qualifications...

Create job alert

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

Similar Jobs

AI Ops

Dallas, Texas

•

7d ago

AI OpsIntroduction:Our client is looking for a Senior ML Platform Engineer to design, build, and operationalize an enterprise ML platform on AWS SageMaker Unified Studio. The successful candidate will play a key role in migrating the organization from a fragmented ML toolchain to a unified, governed platform on AWS Landing Zone 2, covering the full ML lifecycle from data discovery through model deployment and monitoring. Responsibilities:Set up SageMaker Unified Studio platform, including domain

Easy Apply

Contract, Third Party

Depends on Experience

Gurobi Optimization Developer

Dallas, Texas

•

6d ago

A client of Innova Solutions is immediately hiring for a Gurobi Optimization Developer" Position type: Full-Time Contract (W2) Role: Gurobi Optimization Developer Duration: 6+ Months Contract Location : Dallas, TX (Hybrid role) Must have required skills Gurobi Optimization, AWS Services, RDBMS Primary Skill Required for the Role: Experience with Gurobi optimization solver for building and solving mathematical optimization models. Deep expertise in AWS services such as EC2, S3, Lambda, Glue

Easy Apply

Contract

Depends on Experience

MLOps Platform Engineer (SageMaker)

Plano, Texas

•

Today

AVA Consulting is seeking a MLOps Platform Engineer (SageMaker) Location: Plano, TX U.S. Citizens and those authorized to work in the U.S. are encouraged to apply. We are unable to sponsor at this time. Company Background: Our client, a major employer in the area, is looking for a MLOps Platform Engineer (SageMaker) to be part of its team in its North American operations. Job Description: Clients Enterprise Platforms team is looking for a Senior ML Platform Engineer to design, build, and operati

Easy Apply

Contract

Depends on Experience

MLOps

Hybrid in Plano, Texas

•

17d ago

Position: MLOps Platform Engineer (SageMaker) Location: Plano, TX Duration : 12+ Months Job Description: RM NOTES: Export Control form would be required but at the time of onboarding only and not required during submission. This position is withEnterprise Analytical Data & IntegrationTeam and the hiring manager is looking to onboard MLOpsPlatform Engineer (Sagemaker) who is expert in Sagemaker (key skillset) and AWS. Local candidates preferred, 12 months contract with extension, Onsite role.

Easy Apply

Contract

60 - 80

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-0626
Posted 30+ days ago

Fitment

Dice Job Match Score™

🎯 Assessing qualifications...

Create job alert

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

Similar Jobs

AI Ops

Dallas, Texas

•

7d ago

Easy Apply

Contract, Third Party

Depends on Experience

Gurobi Optimization Developer

Dallas, Texas

•

6d ago

Easy Apply

Contract

Depends on Experience

MLOps Platform Engineer (SageMaker)

Plano, Texas

•

Today

Easy Apply

Contract

Depends on Experience

MLOps

Hybrid in Plano, Texas

•

17d ago

Easy Apply

Contract

60 - 80