Role title: Data Scientist
Location: Waterside, UK
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 (e.g., 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 (e.g., 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 (e.g., unit tests, regression tests) to ensure the robustness of operationally critical decision-support products
Deliver features to harden an algorithm against edge cases in the 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, gurobietc.) 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)
Key interfaces
Lead Product Data Scientist
Other Data Scientists
Business stakeholders and users
Software engineers (front-end, back-end, DevOps, data engineers)
Product & change managers
BA Digital teams (e.g., architects, application support managers)
External partners and third parties, as required
ODS Leadership (Head of Data & Analytics, Head of iOps& Optimisation, Director of ODS)
Key performance indicators
Model accuracy, performance, and runtime (precision, recall, accuracy)
Time to develop and deploy features and models
Data ingestion & processing efficiency and robustness
Code quality and robustness (e.g., unit test coverage)
Collaboration and cross-functional teamwork
Behaviours and attitude
I'm a role model for all BA brand behaviours and ways of working –I walk the talk
I exude a can-do attitude (best of BA)
I'm flexible and agile, always ready to adapt when things don't go to plan
I'm an ambassador for BA and my team
I role model our Leadership Behaviours
My core traits
Systems thinking
Detail oriented while understanding the big picture
Curious, self-motivated, proactive, and action-oriented
Creative and innovative
Resilient and flexible in light of changing priorities and approached
Data-driven
Pragmatic
Collaborative
A true believer in the power of using data to drive better decision making
A technologist, interested in keeping up with the latest and greatest in software development, optimization, and machine learning
Commitment to delivering business value