Lead Data Scientist

Vidoori•Hyattsville, MD

11h

About The Position

We are seeking a Lead Data Scientist AI/ML to provide technical leadership across artificial intelligence and machine learning initiatives and deliver reliable, scalable, and impactful data-driven solutions. This role is responsible for defining AI/ML strategy, leading complex modelling projects, developing team capability, and partnering with product, technology, commercial, and business stakeholders. The ideal candidate combines strong expertise in machine learning, statistics, software engineering, and data platforms with proven leadership skills and the ability to translate advanced AI/ML capabilities into practical business outcomes.

Requirements

Significant experience in data science, artificial intelligence, machine learning, statistics, quantitative analysis, or a related technical discipline, including experience leading AI/ML projects or teams
Proven experience applying machine learning and statistical techniques to solve complex business, customer, or operational problems
Strong understanding of supervised and unsupervised learning, deep learning, predictive modelling, feature engineering, model evaluation, experimentation, and statistical inference
Practical experience with Python and common data science, machine learning, and deep learning libraries and frameworks
Experience working with SQL, relational or non-relational databases, data warehouses, data lakes, and large or complex datasets
Experience taking AI/ML models or analytical solutions from development through deployment, monitoring, governance, and ongoing improvement
Experience managing or mentoring data scientists or machine learning engineers through coaching, technical review, professional development, and team planning
Ability to communicate complex artificial intelligence and machine learning concepts, model limitations, analytical findings, and business recommendations clearly to both technical and non-technical stakeholders
Strong problem-solving, critical thinking, decision-making, organisational, and stakeholder management skills
Strong understanding of data governance, information security, data protection, model risk, AI ethics, and responsible artificial intelligence principles
High attention to detail balanced with the ability to take a strategic, organisation-wide view of AI/ML and analytical delivery

Nice To Haves

Experience leading distributed or cross-functional data science, artificial intelligence, and machine learning teams
Experience with generative AI, large language models, prompt engineering, retrieval-augmented generation, natural language processing, computer vision, time series forecasting, recommendation systems, or optimisation
Experience with cloud data and machine learning platforms, including AWS, Microsoft Azure, or Google Cloud
Experience with MLOps, model governance, feature stores, experiment tracking, model monitoring, model registries, and automated machine learning workflows
Experience working with distributed data processing technologies, data pipelines, APIs, software engineering practices, and modern data platforms
Experience with containerisation, orchestration, infrastructure as code, CI/CD, and production-grade machine learning services
Relevant degree or professional qualification in artificial intelligence, machine learning, data science, computer science, statistics, mathematics, engineering, or a related field
Research experience, published work, patents, or contributions to open-source artificial intelligence and machine learning projects
Experience working in an environment with formal information security, data protection, quality, responsible AI, or regulatory requirements

Responsibilities

Lead, mentor, and develop data scientists, machine learning engineers, and related analytical specialists, promoting a collaborative, inclusive, and high-performing culture
Define and implement artificial intelligence and machine learning strategies, standards, methodologies, and best practices aligned with organisational objectives
Lead the delivery of AI/ML projects across the full lifecycle, including problem definition, data preparation, feature engineering, model development, validation, deployment, monitoring, and continuous improvement
Partner with Product Management, Engineering, Data Engineering, Analytics, Operations, and business stakeholders to identify opportunities and define measurable outcomes for AI and machine learning solutions
Provide technical leadership on supervised and unsupervised learning, deep learning, generative AI, natural language processing, computer vision, predictive modelling, experimentation, and optimisation initiatives
Evaluate emerging artificial intelligence and machine learning technologies, tools, frameworks, and foundation models to identify opportunities for innovation and competitive advantage
Translate complex AI/ML concepts, model behaviour, and analytical findings into clear recommendations for technical and non-technical stakeholders
Establish and maintain standards for data quality, model development, validation, reproducibility, documentation, explainability, and responsible use of artificial intelligence
Review analytical approaches, model architectures, code, assumptions, performance metrics, and results to ensure accuracy, robustness, scalability, and business relevance
Oversee the development, deployment, and ongoing performance monitoring of machine learning models, AI services, and data products
Identify and manage risks relating to bias, fairness, privacy, security, explainability, data protection, model drift, hallucination, and regulatory compliance
Promote the use of MLOps and software engineering practices, including version control, automated testing, continuous integration, model registries, experiment tracking, and infrastructure automation
Support recruitment, onboarding, performance management, career development, and succession planning for data science and machine learning team members
Manage priorities, delivery plans, dependencies, resources, and risks across multiple AI/ML initiatives
Identify opportunities to improve data availability, modelling techniques, AI/ML tooling, automation, model operations, and team productivity
Provide regular reporting to senior leadership on project progress, business impact, model performance, team capacity, technical risks, and future AI/ML requirements