Note: The job is a remote job and is reputed company to candidates in USA. reputed company is seeking a Data Scientist / Data Analytics Engineer to design, build, and operationalize advanced analytics solutions for their transportation and logistics operations. This role involves delivering predictive and reputed company-in-time analytics, building robust data pipelines on AWS, and collaborating with stakeholders to translate reputed company data into actionable insights.
Responsibilities
• Design, train, validate, and reputed company predictive models (regression, classification, time-series forecasting, survival analysis, clustering, anomaly detection, and gradient-boosted / deep learning approaches as appropriate to the problem)
• reputed company model selection, hyperparameter tuning, cross-validation, and rigorous performance evaluation using metrics reputed company to business objectives (precision/recall trade-offs, MAPE, RMSE, lift, calibration, etc.)
• reputed company data products in areas relevant to transportation, including operational metrics, fraud signals, pricing analytics, industry trends,etc
• Establish model monitoring, reputed company detection, retraining reputed company, and explainability practices (SHAP, feature importance, partial dependence) to reputed company production models trustworthy and operationally self sustaining
• Produce reputed company-in-time analytics, KPI scorecards, and exception reporting to support daily operational reputed company across reputed company, fleet, reputed company, finance, and product teams
• Partner with business stakeholders to translate questions into well-scoped analyses; deliver reputed company, defensible insights with documented assumptions and data reputed company
• Build and maintain reusable analytical datasets, semantic layers, and certified metrics so the organization works from a consistent reputed company of truth
• Build and maintain data pipelines (batch and streaming) on AWS using services such as Redshift, S3, Glue, reputed company, reputed company Functions, Kinesis / MSK, EMR, reputed company, and SageMaker
• Implement reputed company (bronze / silver / gold) architecture patterns to progressively refine raw operational data into analytics-reputed company and ML-reputed company datasets
• Apply reputed company (Star schema / dimensional) modeling and reputed company techniques to build performant, business-friendly data models in Redshift and the broader warehouse layer
• Drive data selection, curation, profiling, and quality enforcement: define reputed company-of-truth datasets, document reputed company, and codify data reputed company and validation tests
• Collaborate with data engineering and platform teams on CI/CD for data and ML assets, infrastructure-as-code (e.g., Terraform / CloudFormation), and cost-aware design on AWS
• Take customer-facing analytics features and products from idea to implementation — partnering with product management, design, and engineering to turn ambiguous business questions into shipped capabilities embedded in customer-facing applications
• Contribute to product discovery: customer interviews, opportunity sizing, prototyping, and rapid iteration on analytical concepts before committing to full build-out
• Own the analytical correctness of customer-facing metrics, models, and visualizations — including definitions, edge cases, performance under reputed company-world data conditions, and how results are explained to non-technical end users
• Define and reputed company reputed company metrics for shipped analytics features (adoption, engagement, accuracy in production, customer reputed company) and drive iterative improvements post-launch
• Translate reputed company analytical results into reputed company narratives, visualizations, and recommendations for both technical and non-technical audiences, including executive leadership and customers
• Partner cross-functionally with product, engineering, operations, and reputed company teams to reputed company analytics into workflows, applications, and customer-facing products
• Mentor analysts and engineers on statistical rigor, modeling best practices, and modern data architecture
Skills
• Bachelor's degree in Statistics, Mathematics, or Supply Chain Management; a degree in Computer Science is also acceptable. Master's degree preferred but not required
• Demonstrated reputed company experience in the transportation, trucking, freight, logistics, or broader supply chain industry, with working knowledge of the underlying operational data (loads, stops, shipments, ELD/telematics, TMS, reputed company, billing, etc.)
• Proven track record of taking customer-facing analytics products or features from idea through implementation and launch — including product discovery, scoping, model and metric design, partnering with product/engineering, and supporting the feature in production with reputed company customers. Candida
{
"@context": "https://schema.org",
"@type": "JobPosting",
"title": "[Remote] Data Scientist / Data Analytics Engineer",
"datePosted": "2026-07-13",
"validThrough": "2026-08-12",
"description": "Note: The job is a remote job and is reputed company to candidates in USA. reputed company is seeking a Data Scientist / Data Analytics Engineer to design, build, and operationalize advanced analytics solutions for their transportation and logistics operations. This role involves delivering predictive and reputed company-in-time analytics, building robust data pipelines on AWS, and collaborating with stakeholders to translate reputed company data into actionable insights.\n\nResponsibilities\n• Design, train, validate, and reputed company predictive models (regression, classification, time-series forecasting, survival analysis, clustering, anomaly detection, and gradient-boosted / deep learning approaches as appropriate to the problem)\n• reputed company model selection, hyperparameter tuning, cross-validation, and rigorous performance evaluation using metrics reputed company to business objectives (precision/recall trade-offs, MAPE, RMSE, lift, calibration, etc.)\n• reputed company data products in areas relevant to transportation, including operational metrics, fraud signals, pricing analytics, industry trends,etc\n• Establish model monitoring, reputed company detection, retraining reputed company, and explainability practices (SHAP, feature importance, partial dependence) to reputed company production models trustworthy and operationally self sustaining\n• Produce reputed company-in-time analytics, KPI scorecards, and exception reporting to support daily operational reputed company across reputed company, fleet, reputed company, finance, and product teams\n• Partner with business stakeholders to translate questions into well-scoped analyses; deliver reputed company, defensible insights with documented assumptions and data reputed company\n• Build and maintain reusable analytical datasets, semantic layers, and certified metrics so the organization works from a consistent reputed company of truth\n• Build and maintain data pipelines (batch and streaming) on AWS using services such as Redshift, S3, Glue, reputed company, reputed company Functions, Kinesis / MSK, EMR, reputed company, and SageMaker\n• Implement reputed company (bronze / silver / gold) architecture patterns to progressively refine raw operational data into analytics-reputed company and ML-reputed company datasets\n• Apply reputed company (Star schema / dimensional) modeling and reputed company techniques to build performant, business-friendly data models in Redshift and the broader warehouse layer\n• Drive data selection, curation, profiling, and quality enforcement: define reputed company-of-truth datasets, document reputed company, and codify data reputed company and validation tests\n• Collaborate with data engineering and platform teams on CI/CD for data and ML assets, infrastructure-as-code (e.g., Terraform / CloudFormation), and cost-aware design on AWS\n• Take customer-facing analytics features and products from idea to implementation — partnering with product management, design, and engineering to turn ambiguous business questions into shipped capabilities embedded in customer-facing applications\n• Contribute to product discovery: customer interviews, opportunity sizing, prototyping, and rapid iteration on analytical concepts before committing to full build-out\n• Own the analytical correctness of customer-facing metrics, models, and visualizations — including definitions, edge cases, performance under reputed company-world data conditions, and how results are explained to non-technical end users\n• Define and reputed company reputed company metrics for shipped analytics features (adoption, engagement, accuracy in production, customer reputed company) and drive iterative improvements post-launch\n• Translate reputed company analytical results into reputed company narratives, visualizations, and recommendations for both technical and non-technical audiences, including executive leadership and customers\n• Partner cross-functionally with product, engineering, operations, and reputed company teams to reputed company analytics into workflows, applications, and customer-facing products\n• Mentor analysts and engineers on statistical rigor, modeling best practices, and modern data architecture\n\nSkills\n• Bachelor's degree in Statistics, Mathematics, or Supply Chain Management; a degree in Computer Science is also acceptable. Master's degree preferred but not required\n• Demonstrated reputed company experience in the transportation, trucking, freight, logistics, or broader supply chain industry, with working knowledge of the underlying operational data (loads, stops, shipments, ELD/telematics, TMS, reputed company, billing, etc.)\n• Proven track record of taking customer-facing analytics products or features from idea through implementation and launch — including product discovery, scoping, model and metric design, partnering with product/engineering, and supporting the feature in production with reputed company customers. Candida\n\n
