Senior Data Scientist

Location: London, UK (Hybrid)

Employment type: Contract

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 (e.g., strict typing, classes, testing).
• Building 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 (e.g., unit tests, regression tests) to ensure the robustness of operationally critical decision-support products.
• Delivering features to harden an algorithm against edge cases in the operation and in data.
• Conducting analysis to quantify the adoption and value-capture from a decision-support product.
• Engaging with business stakeholders to collect requirements and get feedback.
• Contributing to conversations on feature prioritisation and roadmap, with an understanding of the trade-off between speed vs. long-term value.
• Understanding and integrating the product into existing business processes, and contributing to the development and adoption of new business processes leveraging a decision-support product.
• Communicating 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).