Lead Data Scientist

Vidoori Inc.

. Lead, mentor, and develop data scientists, machine learning engineers, and related analytical specialists .

Posted 10/2/2026full-timeUnited StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading AI/ML projects, including model development, deployment, and monitoring, while ensuring adherence to data governance and ethical AI principles. Proficient in mentoring teams and translating complex analytical findings into actionable insights for diverse stakeholders.

Highest-signal resume keywords

AI/ML Project LeadershipSupervised And Unsupervised LearningPython ProgrammingData Governance And SecurityMLOps Practices

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ScienceMachine LearningDeep LearningPredictive ModellingFeature EngineeringStatistical InferenceModel EvaluationGenerative AINLPComputer Vision

Soft Skills

MentoringCommunicationCollaborationProblem SolvingLeadership

Tools & Technologies

SQLData WarehousesData LakesCI/CDAPIsContainerisationOrchestrationInfrastructure As CodeAutomated TestingVersion Control

Industry Keywords

Artificial IntelligenceMachine LearningData ProtectionModel RiskResponsible AIData QualityModel GovernanceExperiment TrackingTechnical LeadershipData Preparation

Tech Stack

Tools & technologies

CloudPythonSQL

About the role

Key responsibilities & impact

Lead, mentor, and develop data scientists, machine learning engineers, and related analytical specialists
Define and implement artificial intelligence and machine learning strategies, standards, methodologies, and best practices
Lead AI/ML projects across 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
Provide technical leadership across supervised and unsupervised learning, deep learning, generative AI, NLP, computer vision, predictive modelling, experimentation, and optimisation
Evaluate emerging AI/ML technologies, tools, frameworks, and foundation models
Translate complex AI/ML concepts and analytical findings into recommendations
Establish standards for data quality, model development, validation, reproducibility, documentation, explainability, and responsible AI
Review analytical approaches, model architectures, code, assumptions, metrics, and results
Oversee development, deployment, and performance monitoring of ML models, AI services, and data products
Identify and manage risks involving bias, fairness, privacy, security, explainability, data protection, model drift, hallucination, and regulatory compliance
Promote MLOps and software engineering practices including version control, automated testing, CI, model registries, experiment tracking, and infrastructure automation
Support recruitment, onboarding, performance management, career development, and succession planning
Manage priorities, delivery plans, dependencies, resources, and risks across AI/ML initiatives
Report regularly to senior leadership on project progress, business impact, model performance, team capacity, technical risks, and future requirements

Requirements

What you’ll need

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
Ability to communicate complex AI/ML concepts and analytical findings to technical and non-technical stakeholders
Strong understanding of data governance, information security, data protection, model risk, AI ethics, and responsible artificial intelligence principles
Preferred: experience with generative AI, large language models, prompt engineering, retrieval-augmented generation, NLP, computer vision, cloud platforms, MLOps, model governance, distributed data processing, APIs, containerisation, orchestration, infrastructure as code, CI/CD, and production-grade ML services
Relevant degree or professional qualification is preferred, not required