Lead Data Scientist

Job DescriptionJob DescriptionSenior Lead Data ScientistLocation: USA - Chicago, Dallas, Atlanta, NY, Santa ClaraExperience: 12 - 18 Years
Total Experience - 12-18 Years&
Minimum 5 years of experience in Python, Artificial Intelligence, Neural Networks, Natural Processing, Computer Vision, machine learning, and data science.& Pre Sales experienceability to communicate really well is mandatory.
We are looking for an experienced& Senior Lead Data Scientist / ML Engineer& with a strong blend of& pre-sales& expertise,& team leadership, and& technical proficiency& across classical machine learning, deep learning, and& generative AI. You will engage in high-level client discussions, drive technical sales strategies, and lead a team to design and implement cutting-edge ML solutions. This is a strategic role requiring both thought leadership and hands-on technical contributions.
Roles ResponsibilitiesKey Responsibilities

1. Pre-Sales Client Engagement

• Collaborate with the sales and business development teams to identify client needs and formulate AI/ML solutions.
• Present technical concepts, project proposals, and proof-of-concepts (POCs) to prospects and clients.
• Translate complex client requirements into actionable project scopes, estimates, and technical proposals.

1. Leadership Team Management

• Provide direction, mentorship, and performance feedback to a team of data scientists and ML engineers.
• Establish best practices in solution design, code reviews, model validation, and production deployment.
• Drive the strategic roadmap for AI initiatives, ensuring alignment with organizational goals and market trends.

1. Classical Machine Learning Statistical Modeling

• Apply classical machine learning techniques (e.g., regression, clustering, decision trees, ensemble methods) to solve diverse business problems.
• Design and optimize data pipelines, feature engineering processes, and model selection strategies.
• Ensure robust model evaluation, tuning, and performance monitoring in production environments.

1. Deep Learning Generative AI

• Develop and maintain deep learning models using frameworks such as TensorFlow or PyTorch for tasks like computer vision, NLP, or recommendation systems.
• Explore and build solutions leveraging generative AI (GANs, VAEs, or transformer-based architectures) for innovative product features and services.
• Champion research and experimentation with state-of-the-art AI models, staying ahead of industry advances.

1. Project Delivery MLOps

• Lead end-to-end ML project lifecycles, from data exploration and model development to deployment and post-launch maintenance.
• Implement MLOps best practices (CI/CD, containerization, model versioning) on cloud or on-premise infrastructures.
• Collaborate with DevOps and engineering teams to integrate ML solutions seamlessly into existing systems.

1. Stakeholder Management Communication

• Serve as a key technical advisor to executive leadership, product managers, and client teams.
• Communicate complex AI/ML findings in clear, actionable terms to both technical and non-technical audiences.
• Advocate data-driven decision-making and foster a culture of innovation within the organization.

Required Qualifications

• Education Experience
• Master's or PhD in Computer Science, Data Science, Engineering, or a related field is .
• 12+ years& of relevant industry experience in data science or ML engineering, with& 5+ years& in a leadership or management capacity.
• Technical Expertise
• Pre-Sales: Demonstrated experience in client-facing roles, solutioning, and proposal development.
• Classical ML: Skilled in traditional algorithms (regression, classification, clustering, etc.) and statistical methods.
• Deep Learning: Hands-on expertise with frameworks (e.g., TensorFlow, PyTorch) for CNNs, RNNs, transformer architectures, etc.
• Generative AI: Practical exposure to GANs, VAEs, or large models, with a track record of building generative models.
• MLOps: Familiarity with CI/CD pipelines, Docker/Kubernetes, and cloud platforms (AWS, Azure, GCP).
• Leadership Communication
• Proven ability to mentor and lead data science/ML engineering teams to meet project goals.
• Exceptional communication skills for presenting to clients, stakeholders, and executive leadership.
• Experience in agile methodologies and project management, balancing multiple projects simultaneously.

/ Bonus Skills

• Experience in& big data& ecosystems (Spark, Hadoop) for large-scale data processing.
• Background in& NLP,& computer vision, or& recommendation systems.
• Knowledge of& DevOps& tools (Jenkins, GitLab CI, Terraform) for infrastructure automation.
• Track record of published research or contributions to open-source AI projects.