[Hiring] Lead Data Scientist @N Consulting Ltd

Role Description

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.

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.
• Oversee modelling and robust implementation of features contributing to an operations decision-support product.
• Ensure that features integrate seamlessly into the product’s technical stack (data ingestion, user interface, orchestration) as well as the business process and use case.

Accountabilities

• Understand a business problem and its component processes end to end, identifying opportunities to make decisions more optimally leveraging decision-support tooling.
• Conduct analyses and visualizations to identify valuable opportunities for decision-support and determine trade-offs between different potential feature implementations.
• Prototype advanced machine learning and optimization models to prove the value of a use case and approach (in Python).
• Deliver features to industrialize machine learning and optimization models in Python using best-practice software principles.
• Build automated, robust data cleaning pipelines that follow software best-practices (in Python).
• Implement integrations between the core algorithm (machine-learning or optimization) and a workflow orchestration paradigm such as Dagster.
• Implement software in a cloud-based deployment pipeline with Continuous Integration / Continuous Deployment (CI/CD) principles.
• Build 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, understanding the trade-off between speed vs. long-term value.
• Understand and integrate the product into existing business processes, contributing 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.

Skills/capabilities

• Strong knowledge of machine learning and optimization techniques, including 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 visualize the outcomes (e.g., seaborn).
• Proficient in working with cloud platforms (AWS preferred), code versioning (Git), and 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 containerized solutions (e.g., Docker, ECS) is nice to have.
• Experience in code testing (unit, integration, end-to-end tests).
• Strong data engineering skills in SQL and Python.
• Proficient in the 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 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

• 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.

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 approaches
• 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

Interview Questions

Ask the candidate the following questions and share the summary along with the candidate resume:

• Tell me about an optimisation problem you have solved?
• What was the business problem and the context of it?
• Explain what the measure was?
• What were the constraints?
• Which algorithm did you use to solve this?
• What was your end result?