About Careertakes

👉 Important disclosure: Careertakes is a third-party recruiting platform supporting this hiring process. If selected, you will be employed directly by our client, Software Development.

Applicants for this role may also receive access to additional matched opportunities through the Careertakes platform.

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About the Client

Confidential Client builds real‑time pricing solutions for freight transportation providers (truckload and LTL carriers, brokers and forwarders). Their platform integrates with quoting workflows to return production prices set by machine‑learning models trained on each provider’s history. The team is small and cross‑functional: modelers run experiments, evaluate results, and collaborate directly with customers.

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The Role

This is an applied Data Scientist role focused on pricing. You will own parts of the pricing models end‑to‑end — from the data used for training to the prices served in production — and report to the VP of Data Science. The work directly impacts revenue and customer outcomes; we value clear, practical solutions that move business metrics.

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What You’ll Do

• Build, retrain and improve production ML models that set prices; keep models calibrated as freight markets move.
• Estimate demand response to price using quote and booking data; validate estimates thoroughly.
• Design, run and analyze A/B experiments for pricing policies with volume‑controlled comparisons.
• Improve optimization and control logic that converts model outputs into prices.
• Define production checks and replacement criteria for newly trained models.
• Investigate shifts in conversion, volume or margin and produce clear evidence‑based writeups for customer pricing teams.
• Write SQL and Python daily; convert analyses into maintained views, reports and dashboards.

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What We’re Looking For

• 3+ years building predictive models that ran in production and were measured by business outcomes.
• Experience in pricing, revenue management, or demand estimation (any industry).
• Freight industry knowledge (LTL or truckload): lanes, accessorials, carrier economics or LTL freight class.
• Background in optimization or control (constrained optimization, feedback control, or similar).
• Strong Python for modeling (pandas, scikit‑learn and modern ML libraries) and strong SQL.
• Working knowledge of causal inference and experiment design (confounding, selection bias, per‑unit metric pitfalls).
• Experience with probability calibration and evaluating models beyond offline accuracy.
• Habit of specifying validation checks before producing results and communicating failures plainly.
• Clear written communication for non‑technical stakeholders.
• Degree in statistics, mathematics, economics, operations research, engineering, computer science or related quantitative field, or equivalent experience.

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Nice to Have

• Customer‑facing experience in logistics or freight.
• Experience building and running AWS infrastructure for data and ML workloads.
• Some Go (enough to read the service that serves your model).

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Location & Work Type

This role is full‑time and telecommute‑friendly. Applicants should be able to work with teams and customers across the U.S.; specific location or state eligibility will be confirmed during the process.

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Equal Opportunity & Hiring Transparency

Careertakes and our client are Equal Opportunity Employers committed to building a diverse and inclusive workforce. We prohibit discrimination or harassment of any kind. To support a fair and efficient hiring process, AI tools may be used to assist with application review or resume screening. These tools do not replace human decision-making. Final hiring decisions are made by people.

If you have questions about how your data is used, please contact us directly.