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About This Role
Position Summary
We are seeking a Data Scientist / Data Analytics Engineer to design, build, and operationalize advanced analytics solutions that drive measurable outcomes across our transportation and logistics operations. This role is responsible for delivering both predictive analytics (forecasting, classification, anomaly detection, optimization) and point\-in\-time analytics (operational dashboards, KPI reporting, ad\-hoc investigations) for internal stakeholders and customers. The successful candidate blends deep statistical and modeling expertise with hands\-on data engineering skills on AWS, and brings domain fluency in trucking, freight, brokerage, payment factoring, fleet operations, and broader supply chain workflows.
The role sits at the intersection of data science, data engineering, and product delivery. You will own problems end\-to\-end: framing the question with stakeholders, selecting and curating data, building robust pipelines, training and validating models, productionizing them on AWS, and ensuring the outputs are trusted, explainable, and actionable.
Key Responsibilities
Predictive Analytics \& Modeling
- 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).
- 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,etc.
- Establish model monitoring, drift detection, retraining cadence, and explainability practices (SHAP, feature importance, partial dependence) to keep production models trustworthy and operationally self sustaining.
Point\-in\-Time \& Operational Analytics
- Produce point\-in\-time analytics, KPI scorecards, and exception reporting to support daily operational decisions across dispatch, fleet, customer success, finance, and product teams.
- Partner with business stakeholders to translate questions into well\-scoped analyses; deliver clear, defensible insights with documented assumptions and data lineage.
- Build and maintain reusable analytical datasets, semantic layers, and certified metrics so the organization works from a consistent source of truth.
Data Engineering \& Platform
- Build and maintain data pipelines (batch and streaming) on AWS using services such as Redshift, S3, Glue, Lambda, Step Functions, Kinesis / MSK, EMR, Athena, and SageMaker.
- Implement medallion (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 related techniques to build performant, business\-friendly data models in Redshift and the broader warehouse layer.
- Drive data selection, curation, profiling, and quality enforcement: define source\-of\-truth datasets, document lineage, and codify data contracts 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.
Customer\-Facing Analytics Products
- 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 instrument success metrics for shipped analytics features (adoption, engagement, accuracy in production, customer outcomes) and drive iterative improvements post\-launch.
Collaboration \& Communication
- Translate complex 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.
Required Qualifications
- 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, 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\-end.
- Strong applied experience building advanced analytical models end\-to\-end, including problem framing, data selection and curation, feature engineering, model training and validation, and deployment.
- Hands\-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 SageMaker.
- Proficiency 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 appropriate.
- Experience designing and operating production data pipelines, with a clear understanding of orchestration, idempotency, observability, and data quality.
- Solid grounding in statistical methods: hypothesis testing, experimental design, regression, time\-series, and uncertainty quantification.
Preferred Qualifications
- Master's degree in Statistics, Mathematics, Operations Research, Supply Chain, Computer Science, or a closely related quantitative field.
- Experience implementing medallion architecture (bronze / silver / gold) in a cloud data lakehouse or warehouse environment.
- Experience designing STARR / star\-schema dimensional models for analytics consumption.
- Experience with streaming and event\-driven data (Kinesis, Kafka/MSK) for near\-real\-time analytics on transportation events.
- Experience deploying and monitoring ML models in production using SageMaker, MLflow, or equivalent MLOps tooling.
- Familiarity with BI / visualization tools (e.g., QuickSight, Power BI, Looker) and semantic layer / metrics layer concepts.
- Exposure to optimization and operations research techniques (linear / mixed\-integer programming, routing, network flow) applied to transportation problems.
- Experience 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 processes.
Core Competencies
- Analytical rigor — comfortable defending methodology, assumptions, and uncertainty to a skeptical audience.
- Business pragmatism — chooses the simplest model that solves the problem and ships value quickly.
- Product mindset — thinks beyond the model to the end\-user experience; comfortable iterating on customer\-facing analytics features alongside product and engineering partners.
- Engineering discipline — writes clean, version\-controlled, testable code; values reproducibility and lineage.
- Stakeholder partnership — listens well, scopes tightly, and communicates trade\-offs clearly.
- Curiosity and ownership — investigates anomalies, challenges data quality, and drives issues to root cause.
Representative Tech Environment
- Cloud \& Data Platform: AWS (Redshift, S3, Glue, Lambda, Step Functions, Athena, Kinesis, EMR, SageMaker).
- Modeling \& Analysis: Python (pandas, scikit\-learn, statsmodels, XGBoost/LightGBM, PyTorch/TensorFlow), SQL, Jupyter.
- Data Architecture: Medallion (bronze/silver/gold), STARR / dimensional models, data contracts, lineage tooling.
- Orchestration \& DevOps: Airflow / Step Functions, Git, CI/CD, Terraform or CloudFormation.
- Visualization: QuickSight, Power BI, or Looker (as applicable).
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Transflo, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 808 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Transflo AI Hiring
Transflo has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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