We are looking to hire a Data Scientist role for one of our renowned IT client in Waterside,UK. This is a contract role and hybrid work opportunity.
Role purpose
This role is responsible for developing industrialized optimisation and machine learning models as part of a full-stack product squad that delivers operations decision-support software.
Scope
As a key member of a product squad and reporting to the Lead Product Data Scientist, a Data Scientist will develop data pipelines, machine learning models, and complex optimization models in the ODS software product suite.
The Data Scientist oversees modelling and robust implementation of features contributing to an operations decision-support product.
In developing a product’s core algorithm, the full-stack Data Scientist role will ensure that their features integrate seamlessly into the product’s technical stack (data ingestion, user interface, orchestration) as well as the business process and use case (to maximize impact and value realization).
Accountabilities
- The Data Scientist has full-stack accountabilities across the full value chain of building an industrialized data-science software product:
- Understanding a business problem and its component processes end to end, and identifying opportunities to make decisions more optimally leveraging decision-support tooling.
- Efficiently conducting analyses and visualizations to identify valuable opportunities for decision-support and to determine trade-offs between different potential feature implementations.
- Prototyping advanced machine learning and optimization models to prove the value of a use case and approach (in Python).
- Delivering features to industrialize machine learning and optimization models in Python using best-practice software principles (strict typing, classes, testing).
- Build automated, robust data cleaning pipelines that follow software best-practices (in Python).
- Implementing integrations between the core algorithm (machine-learning or optimization) and a workflow orchestration paradigm such as Dagster.
- Implementing software in a cloud-based deployment pipeline with Continuous Integration / Continuous Deployment (CI/CD) principles.
- Building logging, error handling, and automated tests (unit tests, regression tests) to ensure the robustness of operationally critical decision-support products.
- Deliver features to harden an algorithm against edge cases in operation and in data.
- Conduct analysis to quantify the adoption and value-capture from a decision-support product.
- Engage with business stakeholders to collect requirements and get feedback.
- Contribute to conversations on feature prioritisation and roadmap, with an understanding of the trade‑off between speed vs. long‑term value.
- Understand and integrate the product into existing business processes, and contribute to the development and adoption of new business processes leveraging a decision-support product.
- Communicate feature and modeling approach, trade-offs, and results with the internal team and business stakeholders.
- The Data Scientist is also accountable for ways of working fit for an Agile cross‑functional development squad, including:
- Using Git-versioning best practices for version control.
- Contributing and reviewing pull‑requests and product / technical documentation.
- Giving input on prioritization, team process improvements, optimizing technology choices.
- Working independently and giving predictability on delivery timelines.
Skills / capabilities
- Strong knowledge of either machine learning and optimization techniques, incl. supervised (regression, tree methods, etc.), unsupervised (clustering) learning, and operations research (linear, mixed integer programming, heuristics).
- Fluent in Python (required) and other programming languages (preferred) with strong skills in applying DS, ML, and OR packages (scikit-learn, pandas, numpy, gurobi etc.) to solve real‑life problems and visualise the outcomes (e.g. seaborn).
- Proficient in working with cloud platforms (AWS preferred), code versioning (Git), experiment tracking (e.g. MLflow).
- Experience with cloud‑based ML tools (e.g. SageMaker), data and model versioning (e.g. DVC), CI/CD (e.g. GitHub Actions), workflow orchestration (e.g. Airflow/Dagster) and containerised solutions (e.g. Docker, ECS) nice to have.
- Experience in code testing (unit, integration, end‑to‑end tests).
- Strong data engineering skills in SQL and Python.
- Proficient in use of Microsoft Office, including advanced Excel and PowerPoint skills.
- Advanced analytical skills, including the ability to apply a range of data science and analytic techniques to quickly generate accurate business insights.
- Understanding of the trade‑offs of different data science, machine learning, and optimization approaches, and ability to intelligently select which are the best candidates to solve a particular business problem.
- Able to structure business and technical problems, identify trade‑offs, and propose solutions.
- Communication of advanced technical concepts to audiences with varying levels of technical skills.
- Managing priorities and timelines to deliver features in a timely manner that meet business requirements.
- Collaborative team‑working, giving and receiving feedback, and always seeking to improve team processes.
Qualifications / experience
- Master’s degree or greater in data science, ML, or operational research, or 2+ years of highly relevant industry experience (required).
- 0‑2 years working on production ML or optimization software products at scale (required).
- Experience in developing industrialized software, especially data science or machine learning software products (preferred).
- Experience in relevant business domains (transportation, airlines, operations, network problems) (preferred).
#J-18808-Ljbffr