About Zeus
Zeus is a leading logistics technology company focused on digitising, automating, and optimising supply chains through AI-powered solutions. Our integrated platforms Zeus Command, Zeus Command AI, and Zeus Connect streamline freight operations, enhance efficiency, reduce costs, and provide real-time visibility.
Founded to modernise the transport and logistics sector, Zeus has grown into one of Europe’s fastest-growing logistics tech companies, working with major enterprises across multiple countries. Backed by advanced AI and sustainable transport solutions, Zeus is shaping the future of logistics.
As we continue to scale, we’re looking for passionate individuals to join our team and help drive innovation in supply chain technology. If you're excited about AI, automation, and revolutionising logistics, Zeus offers an opportunity to work in a fast-paced, dynamic environment with leading industry expertise and state-of-the-art technology.
About this job
The mission
Work with our AI and data team on Pack AI, our AI driven order consolidation and VRP module. Understand each prospect’s unique data and constraints, shape a custom solution, and clearly communicate results. You will own analytical workstreams end-to-end.
What will you do
- Client-centred analysis: Partner with potential customers to unpack business goals, data quirks, and constraints; translate them into a simulation plan and success criteria.
- Data science hands-on: Clean, transform, and analyse large order/delivery datasets; build visualisations and executive-ready summaries (vehicle utilisation, mileage, cost, quantities, OTR).
- Parameter tuning: Design experiments, sweep parameters, evaluate trade-offs, and recommend configurations (e.g., slot windows, min-qty thresholds, SKU segregation).
- API usage & integration: Read docs, consume and stitch APIs (routing, ETAs, data exchange); produce robust scripts/services for repeatable runs.
- Athena AI development support: Build data pipelines, evals, and monitoring for Athena agent workflows; collaborate with engineering team on LLM integration quality and observability.
What great looks like (first 3–6 months)
- You independently run a full simulation cycle (data prep → parameter experiments → recommendations → presentation).
- You deliver clear, written decision memos and visuals stakeholders can validate.
- You improve plan quality on at least one prospect (e.g. higher trailer utilisation or reduced mileage) with a documented recipe.
- You contribute meaningfully to Athena AI agent development (e.g., build an eval suite for tool execution quality, implement a new data pipeline for agent context)
Must-have
- Strong analytical & problem-solving skills: Able to decompose messy, real-world problems and craft tailored solutions.
- Proficient in Python & data science: pandas/Polars, NumPy, scikit-learn, Jupyter, Plotly (or similar)
- Confident with data cleaning, analysis, transformation, and visualisation.
- Excellent communication: Advanced written and spoken English; concise, direct, and audience-appropriate (technical + executive).
- Self-organised, responsible, independent: Prior professional experience; capable of planning your own workload to deadlines.
- API usage & integration: Comfortable reading API docs, handling auth, rate limits, and building reliable consumers/producers.
Nice to have
- Routing/optimisation exposure: NVIDIA cuOpt, VROOM, HERE (or similar); familiarity with multi-drop, time windows, packing rules.
- Spatial data experience: Coordinates, geocoding, map/route visualisation, basic GIS concepts.
- Cloud & delivery awareness: AWS (S3, ECS/Lambda, SNS/SQS), Docker, CI/CD (GitHub Actions), SQL/Postgres.
- TypeScript fluency: Able to read TS and collaborate on data contracts with the Command team.
Our environment
- Python, Polars/pandas, NumPy, scikit-learn, Plotly, SQL/Postgres, NVIDIA cuOpt, HERE APIs, AWS (S3, ECS/Lambda, SNS/SQS), Docker, GitHub Actions, TypeScript/Node (Command services).
Interview process
- 30-min intro (role, your goals)
- Python data task (clean/transform + quick viz)
- API & systems chat (design a small data pull + retries/observability)
- Simulation case (parameter trade-offs; short write-up)
- Stakeholder round (present findings; Q&A)
What we offer
- Private health insurance
- Pension plan
- Central working location
- Hybrid working
- Casual dress code
- Stocked kitchen and as much coffee & tea as you can drink
- Your own Mac or PC laptop and software subscriptions
- Discounted Gym memberships, a variety of virtual classes and seminars free of cost, discount coupons for all the major brands via Charlie HR perks
