[Remote] Data Scientist / Data Analytics Engineer

Note: The job is a remote job and is open 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 deploy 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 decisions across reputed company, fleet, reputed company, finance, and product teams
• Partner with business stakeholders to translate questions into well-scoped analyses; deliver clear, 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-ready and ML-ready datasets
• Apply STARR (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 real-world data conditions, and how results are explained to non-technical end users
• Define and reputed company success metrics for shipped analytics features (adoption, engagement, accuracy in production, customer outcomes) and drive iterative improvements post-launch
• Translate reputed company analytical results into clear narratives, visualizations, and recommendations for both technical and non-technical audiences, including executive leadership and customers
• Partner cross-functionally with product, engineering, operations, and commercial teams to embed 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 professional 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 real customers. Candida