InnoCore Solutions, Inc.

Data Scientist (Automotive Logistics, AWS Cloud)

Dallas, Texas, USA · Contract

Sponsorship not specifiedDetected 112 days ago
PythonGitSQLSnowflakeRedshiftAWSCI/CDMachine LearningTensorFlowPyTorchscikit-learnSparkData EngineeringData ScienceLLMsMLOpsStatisticsForecastingSupply ChainLogisticsERPControlsDemand Planning

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odds of building a lasting career here

40Risky
Cap-exempt (no lottery)0
Sponsors this role90
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70

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Employer immigration record

from this employer's Department of Labor filings

Third-party placement is common here

72% of this employer's visa filings place the worker at a client company rather than at the employer itself, across 10 clients. Your visa, salary band and layoff exposure would follow the employer of record, not the client.Most common: American Airlines · Toyota Motor North America · Capital OneFiles as an H-1B dependent employer.

Green-card follow-through: 100%

Of 6 labor certifications old enough to have been used, 0 expired without the employer filing the next step. Median time from filing to decision: 496 days.91% of their filings were for a worker who already held the job.Only certifications past the 180-day window are counted — recent ones cannot have expired yet.

Files H-1B transfers

21 transfer filings in the last year, covering 21 workers. Median labor-condition decision: 40 days. An employer that already files transfers is one that can take over an existing H-1B.

Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.

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About the role

  • This role is central to improving vehicle and parts supply chain visibility, inventory accuracy, and fulfillment predictability using machine learning, MLOps best practices, and AWS-native services.
  • Incorporate structured and unstructured data from ERP, telematics, dealer orders, production plans, and real-time logistics feeds.
  • Continuously monitor model accuracy and improve forecasts based on error analysis, drift detection, and business input.

Responsibilities

  • Design, train, and deploy advanced time series and regression-based forecasting models (e.g., ARIMA, Prophet, XGBoost, LSTM) for vehicle demand, part shipments, and inventory needs.
  • 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.
  • Implement SageMaker Model Monitor, SageMaker Clarify, and CloudWatch for continuous model performance, bias, and drift monitoring.
  • Lead the design of scalable ETL/ELT pipelines using AWS Glue, Apache Spark, and AWS Step Functions.
  • Manage data ingestion from diverse sources (S3, Redshift, Snowflake, RDS, Kinesis) with robust governance and lineage tracking.
  • Develop AI-driven decision support tools using classification models, clustering, anomaly detection, and explainable AI (XAI).
  • Collaborate with business teams to embed AI recommendations into dashboards, alerts, or APIs.

Requirements

  • Strong experience in Python, with ML libraries such as scikit-learn, TensorFlow, PyTorch, and XGBoost.
  • 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.
  • Experience implementing MLOps pipelines in production environments.
  • 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.
  • Advanced degree (MS or PhD) in a quantitative field including but not limited to Statistics, Computer Science/Data Science, Industrial Engineering, or Applied Mathematics.

Benefits

  • 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.
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