Job Description

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

Skills Required