FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in owning and operating live machine learning systems for dynamic pricing, with a strong focus on probabilistic modeling, decision-making under uncertainty, and production-grade Python development. Collaborates effectively with cross-functional teams to design experiments, monitor performance, and ensure reproducibility in pricing decisions.
Highest-signal resume keywords
Production-Grade PythonProbabilistic ModellingMachine Learning Production ServicesDynamic Pricing SystemsCloud Infrastructure (AWS)
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Probabilistic ModellingDynamic PricingMachine LearningProduction Code DevelopmentExperiment DesignObservability DesignFeedback Loop ManagementContainerised ModelsCI/CDStochastic Optimisation
Soft Skills
CollaborationProblem FormulationDecision-Making
Tools & Technologies
AWSAPIsEvent-Driven MicroservicesAI ToolingInfrastructure as Code
Certifications & Qualifications
PhD in MathsPhD in PhysicsPhD in Computer SciencePhD in Operations Research
Industry Keywords
Algorithmic TradingMarketplacesAd BiddingElasticity ModellingEnergy MarketsCommodities
Tech Stack
Tools & technologiesAWSCloudMicroservicesPython
About the role
Key responsibilities & impact- Own key components of the dynamic pricing system end to end, from design through production operation.
- Choose bets most likely to reduce sales adjustment and demonstrate whether they worked.
- Make components observable, guarded and replayable so incorrect prices are caught before reaching customers.
- Unblock delivery across engineering, platform and commercial teams.
- Own components such as models learning from won and lost deals, experimentation and measurement layers, and live pricing guardrails.
- Design experiments and holdouts for low-volume, high-value deals where standard A/B testing may not be possible.
- Build monitoring, alerting, guardrails and staged releases.
- Detect drift, runaway feedback loops and unexpected price movements requiring review.
- Make pricing decisions reproducible for debugging and regulators.
- Collaborate with the Price Quoting squad and ML Platform to build production-ready models.
- Investigate why sales teams adjust prices and turn those reasons into model inputs or new bets.
- Explain model behaviour and trade-offs to engineering, product and commercial colleagues.
- Redesign recurring pricing workflows with AI, including feedback triage, daily price review and experiment write-ups, deciding which steps remain human.
- Report to the ML Leader for Rosso.
Requirements
What you’ll need- Most of your current role is writing and shipping production code; this is not a people leadership or architecture role where others implement.
- Experience owning a live ML system making automated decisions with money attached, such as algorithmic trading, dynamic pricing, marketplaces, ad bidding, credit or betting.
- Experience handling production failures including feedback loops, silent drift, bad outputs reaching customers and rollbacks.
- Strong probabilistic modelling and decision-making under uncertainty, including pricing, conversion or elasticity modelling and experimentation.
- Ability to formulate problems from first principles and select the simplest effective approach.
- Experience running ML production services on modern cloud infrastructure, including containerised models behind APIs, event-driven microservices, infrastructure as code and CI/CD on AWS or similar.
- Production-grade Python.
- Experience designing for observability, testing and replay.
- Ability to work as a peer with software engineers.
- Track record of moving a commercial metric with product managers and commercial stakeholders.
- Uses AI tooling as a day-to-day work multiplier and understands where it fails.
- Energy experience is not required.
- Bonus: experience in energy, commodities or markets where prices, forecasts and risk interact.
- Bonus: contextual bandits, causal inference or Bayesian methods used in production.
- Bonus: stochastic optimisation or operations research.
- Bonus: PhD in maths, physics, computer science or operations research.
- Able to speak, read and write English fluently.
Benefits
Comp & perks- Stock Options: everyone on the team has ownership in our mission.
- 25 days holiday + public holidays.
- Ability to swap public holidays for ones that matter most to you.
- Extra day off for your birthday.
- Fully remote working, distributed across Europe.
- Up to £1,200 / €1,200 annually for remote setup, co-working passes, equipment, etc.
- Up to £150 / €150 monthly wellbeing budget for therapy, gym memberships, meditation apps, etc.