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Lead Data Scientist
Location
United Kingdom
Posted
64 days ago
Salary
£400 - £450 / day
Seniority
Lead
Job Description
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?
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