What Does A Lead Data Scientist Do?

WHAT DOES A LEAD DATA SCIENTIST DO?

Published: Jan 10, 2026 - The Lead Data Scientist builds and leads a high-performing data science organization that delivers enterprise-wide insights through advanced analytics and a scalable AI/ML framework. This role drives the end-to-end design, deployment, governance, and continuous improvement of AI/ML solutions while ensuring quality, compliance, and collaboration across technical and business teams. The lead also fosters innovation and capability growth and also establishes communities of practice, supports research partnerships, and develops a robust operational model to sustain complex analytical initiatives.

A Review of Professional Skills and Functions for Lead Data Scientist

1. Lead Data Scientist Duties

Architecture Planning: Planning and establishing organisational policies and standards for software development
Software Engineering: Designing and developing quality software, planning data projects, and building analytic and predictive systems
System Architecture: Designing architectures for complex information systems
Project Management: Managing all stages and iterations of software development and data analytics projects
Experience Design: Managing overall user experience design and testing programs
Product Strategy: Managing and planning a product's lifecycle strategy including analysing the business context and user needs
Product Vision: Developing the vision for the product, monitoring the market, building the roadmap for functionality, and communicating this to stakeholders
Idea Coordination: Coordinating the creative process of generating, developing, and prioritising ideas, based on in-depth experience in machine learning and other analytical approaches
Business Support: Supporting the achievement of key business and product objectives
Stakeholder Communication: Ensuring that the status of ideas is communicated back to users, team members, and other stakeholders

2. Lead Data Scientist Details

Model Development: End-to-end ownership and development of deep learning based computer vision models
Process Compliance: Understand and follow IT and company processes for machine learning projects
Problem Analysis: Understand the problem, analyze and develop an approach to solve it
Data Preparation: Collect, analyze, review, clean, organize, and document image datasets efficiently/programmatically
Data Annotation: Responsible for getting the dataset annotated with the help of annotators (internal/external)
Image Processing: Transform images using opencv library and develop scripts to process annotation data/files
Model Customization: Customize standard deep learning based computer vision models for object/keypoint detection/segmentation (using Python and Keras/TensorFlow/pytorch)
Model Training: Train models systematically with a clear rationale behind the approach used
Parameter Tuning: Tune hyperparameters and loss functions
Experiment Tracking: Track training experiments and evaluate and compare models objectively
ML Techniques: Leverage other machine learning approaches like clustering, regression, and classification to solve sub-problems
Code Optimization: Review, refactor, optimize for speed/efficiency, and check in code frequently
Technical Documentation: Capture detailed documentation of data, models, application architecture, and steps to execute code
Cloud Deployment: Manage datasets and deploy models and inference pipelines on the Azure cloud for scaling in production
Status Reporting: Communicate results/status updates to stakeholders once a week

3. Lead Data Scientist Responsibilities

Mission Translation: Translates mission needs into an end-to-end analytical approach to achieve results
Solution Presentation: Present solutions and recommendations to client leadership
Analytics Selection: Determines the appropriate analytics based on the data and the desired outcomes, using techniques including feature detection, statistics, data mining, predictive modeling, machine learning, natural language processing, and business intelligence
Result Interpretation: Interprets the validity of results and communicates the meaning of those results
Scientific Method: Follows a scientific approach to generate value from data, verifying results at each step
Statistical Tuning: Perform statistical analysis and tuning using test results, and transform data science prototypes according to requirements
Data Study: Study appropriate datasets and transform data science prototypes
Algorithm Research: Research and implement appropriate machine learning algorithms and tools
Application Development: Develop machine learning applications according to requirements

4. Lead Data Scientist Accountabilities

Feature Engineering: Selecting features, building and optimizing classifiers using machine learning techniques
Pipeline Building: Building and monitoring robust data cleansing/training/ML application pipelines
Data Mining: Data mining using state-of-the-art methods
Data Collection: Enhancing data collection procedures to include information that is relevant for building analytic systems
Data Verification: Processing, cleansing, and verifying the integrity of data used for analysis
Adhoc Analysis: Doing ad-hoc analysis and presenting results in a clear manner
Result Explanation: Clarify/explain results to senior business stakeholders in business-friendly terms
Team Collaboration: Collaborate with other teams to expose/include ML models in business scenarios such as workflows and apps that consume ML models

5. Lead Data Scientist Functions

Model Leadership: Lead and manage the development, delivery, configuration, maintenance, and support of a portfolio of AI models and related products
Release Management: Manage AI model releases
Metric Tracking: Define, create, and track metrics for various models
Feedback Integration: Take feedback from analysis, end users, and domain experts to manage model calibration, bug fixes, and enhancements
Client Onboarding: Manage new client onboarding by developing a configuration for model pipelines
Team Management: Manage a team of Data Scientists and Data Engineers working on model support
Model Delivery: Manage model delivery to the Production deployment team and coordinate model production deployments
Cross Collaboration: Manage effective collaboration within cross-functional teams
Data Analysis: Dive deep into data, do analysis, and discover patterns/root causes
Insight Generation: Generate insights that drive the product
Vendor Management: Manage deliverables and projects assigned to vendor partners in the AI models and products space

6. Lead Data Scientist Overview

Data Coordination: Work with the engineering team and the architecture teams for data identification and collection, harmonization, and cleansing for data analysis and preparation
Algorithm Selection: Responsible for analysing and identifying appropriate algorithms for the defined problem statement
Input Analysis: Analyse additional data inputs and methods that would improve the results of the models and look for opportunities
Model Development: Responsible for building models that are interpretable, explainable, and sustainable at scale and meet the business needs
Result Visualization: Build visualizations and demonstrate the results of the model to the stakeholders and leadership team
Agile Delivery: Must be conversant with Agile methodologies and tools and have a track record of delivering products in a production environment
Prototype Design: Lead the design of prototypes in the AI factory, partnering with product teams, AI strategists, and other stakeholders throughout the AI development life cycle
Prototype Transformation: Lead and transform data science prototypes

7. Lead Data Scientist Details and Accountabilities

Team Mentoring: Mentor a diverse team of junior engineers in machine learning techniques, tools, and concepts
Leadership Guidance: Provides guidance and leadership to more junior engineers
Tool Exploration: Explore and recommend new tools and processes that can be leveraged across the data preparation pipeline for capabilities and efficiencies
Deployment Integration: Ensure that development and deployment are tightly integrated to maximize the deployment user experience
Code Curation: Curator for all code and binary artifact repositories (containers, compiled code)
Data Collaboration: Work with AI strategists, DevOps, and data engineers/SMEs from the domain to understand how data availability and quality affect model performance
Tech Evaluation: Evaluate open source and proprietary technologies and present recommendations to automate machine learning workflows, model training and versioned experimentation, digital feedback, and monitoring
Innovation Development: Develop and disseminate innovative techniques, processes, and tools that can be leveraged across the AI product development lifecycle

8. Lead Data Scientist Tasks

Team Engagement: Join a group of passionate people committed to delivering happiness to users and to each other
Vision Building: Help build the vision for Core Marketing Science which includes, for example, designing and maintaining a Customer Data Platform and developing predictive models around Attribution, Customer Lifetime Value, and Churn
Plan Execution: Define and execute on a plan to achieve that vision
Pipeline Development: Drive the design, building, and production of new personalization models and data pipelines and oversee the maintenance of existing pipelines
SLA Management: Define and manage SLAs and alerting for production datasets and processes
Data Visualization: Oversee the creation and delivery of high-impact dashboards and data visualizations
Cross Collaboration: Build cross-functional relationships with Business Leaders in Marketing and Product Marketing to understand data needs and deliver on those needs
Culture Stewardship: Continue to enhance and protect the culture of the Zoom Data organization

9. Lead Data Scientist Roles

Team Leadership: Build and lead a team responsible for extracting business insight from enterprise data using advanced analytic techniques
Framework Design: Design, deploy, and continuously evolve the artificial intelligence (AI) / machine learning (ML) framework (tools and processes) for delivering advanced analytics solutions
Strategy Support: Support the definition of the enterprise AI/ML strategy and lead its implementation from a governance, framework, and platform perspective
Solution Integration: Lead technical solution design and integration tasks for data science projects from proof of concepts to final implementation/deployment and monitoring with a focus on scalability and performance
Cross Collaboration: Work in close collaboration with Data Engineers, Data Scientists, Software Engineers, Solution Architects, and Product/Program Managers on the definition, creation, deployment, monitoring, and documentation of ML models/AI solutions
Quality Assurance: Ensure ML and AI solutions are delivered with high-quality standards, meeting industry-leading practices related to ethical and regulatory compliance
Planning Support: Provide advice on estimates and implementation plans to support planning and roadmap activities
Activity Coordination: Coordinate activities and set priorities to meet project deadlines
Operational Modeling: Establish the operational support model for the advanced analytics platform with a mix of internal and external staffing to deliver on the agreed Service Level Agreement (SLA)
Data Innovation: Innovate by promoting a data-centric culture and advocating collaboration across the entire organization
Community Building: Set up and lead the Bombardier AI/ML Community of Practice
Research Leadership: Lead or support research projects related to AI/ML with universities and/or independent research groups to advance team capabilities
External Contracting: Contract external support to complement the team's skills and capacity and deliver the project within timelines
Environment Development: Create a rewarding business environment for the team by continuously developing their skills and delivering projects of increasing complexity

10. Lead Data Scientist Additional Details

Manufacturing Analytics: Work on complex and a variety of data to solve real-world problems in Manufacturing
Model Development: Designing and delivering predictive, forecasting, optimization, and simulation models using statistics, Machine Learning, and AI programs
Algorithm Research: Research and design new algorithms, models, and modules (Data Cleaning, Data Quality, Alert) for Bodhee AI Apps
Project Delivery: Manage end-to-end delivery of 2–3 projects in parallel by using Bodhee features
Insight Presentation: Present impact, insights, outcomes, and recommendations to key business partners and stakeholders
Process Improvement: Identify and fix process gaps
App Management: Manage the delivery of AI Apps
MLOps Architecture: Architect, set up, and enforce MLOps for all deployments
Testing Ownership: Own the technical testing and performance plan for their projects
Debt Mitigation: Identify and proactively tackle technical debt before it grows into debt that requires significant up-front work to resolve
Module Integration: Manage the integration of AI modules with UI and back-end modules in collaboration with the Full-stack Engineering team
Team Management: Manage 5-10 junior Data Scientists, Data Engineers, and Team Leads
Architecture Feedback: Provide feedback on the overall architecture of Bodhee by being part of the Architecture Review Team

Relevant Information

What Does A Data Scientist Do?