[Remote] Data Scientist / Data Analytics Engineer

Note: The job is a remote job and is open to candidates in USA. Transflo 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 point-in-time analytics, building robust data pipelines on AWS, and collaborating with stakeholders to translate complex data into actionable insights.ResponsibilitiesDesign, 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)Lead model selection, hyperparameter tuning, cross-validation, and rigorous performance evaluation using metrics aligned to business objectives (precision/recall trade-offs, MAPE, RMSE, lift, calibration, etc.)Develop data products in areas relevant to transportation, including operational metrics, fraud signals, pricing analytics, industry trends,etcEstablish model monitoring, drift detection, retraining cadence, and explainability practices (SHAP, feature importance, partial dependence) to keep production models trustworthy and operationally self sustainingProduce point-in-time analytics, KPI scorecards, and exception reporting to support daily operational decisions across dispatch, fleet, customer success, finance, and product teamsPartner with business stakeholders to translate questions into well-scoped analyses; deliver clear, defensible insights with documented assumptions and data lineageBuild and maintain reusable analytical datasets, semantic layers, and certified metrics so the organization works from a consistent source of truthBuild and maintain data pipelines (batch and streaming) on AWS using services such as Redshift, S3, Glue, Lambda, Step Functions, Kinesis / MSK, EMR, Athena, and SageMakerImplement medallion (bronze / silver / gold) architecture patterns to progressively refine raw operational data into analytics-ready and ML-ready datasetsApply STARR (Star schema / dimensional) modeling and related techniques to build performant, business-friendly data models in Redshift and the broader warehouse layerDrive data selection, curation, profiling, and quality enforcement: define source-of-truth datasets, document lineage, and codify data contracts and validation testsCollaborate 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 AWSTake 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 applicationsContribute to product discovery: customer interviews, opportunity sizing, prototyping, and rapid iteration on analytical concepts before committing to full build-outOwn 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 usersDefine and instrument success metrics for shipped analytics features (adoption, engagement, accuracy in production, customer outcomes) and drive iterative improvements post-launchTranslate complex analytical results into clear narratives, visualizations, and recommendations for both technical and non-technical audiences, including executive leadership and customersPartner cross-functionally with product, engineering, operations, and commercial teams to embed analytics into workflows, applications, and customer-facing productsMentor analysts and engineers on statistical rigor, modeling best practices, and modern data architectureSkillsBachelor's degree in Statistics, Mathematics, or Supply Chain Management; a degree in Computer Science is also acceptable. Master's degree preferred but not requiredDemonstrated 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, dispatch, 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. Candidates should be prepared to walk through at least one concrete example end-to-endStrong applied experience building advanced analytical models end-to-end, including problem framing, data selection and curation, feature engineering, model training and validation, and deploymentHands-on experience with AWS PaaS / analytics tooling, including Amazon Redshift and other relevant services such as S3, Glue, Lambda, Step Functions, Athena, Kinesis, EMR, and SageMakerProficiency in SQL (advanced window functions, performance tuning on Redshift or comparable MPP warehouses) and at least one analytics-grade programming language — Python strongly preferred — with libraries such as pandas, scikit-learn, statsmodels, XGBoost/LightGBM, and PyTorch or TensorFlow as appropriateExperience designing and operating production data pipelines, with a clear understanding of orchestration, idempotency, observability, and data qualitySolid grounding in statistical methods: hypothesis testing, experimental design, regression, time-series, and uncertainty quantificationMaster's degree in Statistics, Mathematics, Operations Research, Supply Chain, Computer Science, or a closely related quantitative fieldExperience implementing medallion architecture (bronze / silver / gold) in a cloud data lakehouse or warehouse environmentExperience designing STARR / star-schema dimensional models for analytics consumptionExperience with streaming and event-driven data (Kinesis, Kafka/MSK) for near-real-time analytics on transportation eventsExperience deploying and monitoring ML models in production using SageMaker, MLflow, or equivalent MLOps toolingFamiliarity with BI / visualization tools (e.g., QuickSight, Power BI, Looker) and semantic layer / metrics layer conceptsExposure to optimization and operations research techniques (linear / mixed-integer programming, routing, network flow) applied to transportation problemsExperience working with ELD/HOS data, telematics feeds, geospatial data, or TMS / dispatch system data, brokerage data, and general understanding of transportation backoffice operations and business processesCompany OverviewTransflo develops mobile, telematics, and business process automation solutions for the transportation industry. It was founded in 1991, and is headquartered in Tampa, Florida, USA, with a workforce of 201-500 employees. Its website is https://transflo.com.