This role is ONLY OPEN to candidates already based in the US and have valid work authorization
This is a permanent role. No contractors / C2C / C2H please.
If you are based in Dallas or Bay Area - role is Hybrid anything else is Remote with Travel
Interview Process: 5 Rounds of Interview - 2 Tech, 2 Leadership and 1 Sales
Reporting structure: Will report to Associate VP AI & Analytics Sales
Relocation Expenses: Available if you are interested to move to our client's hubs

ABOUT THE ROLE

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.

Responsibilities:

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.

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.

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.

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.

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.

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.

Must Haves:

Education & Experience

Master’s or PhD in Computer Science, Data Science, Engineering, or a related field is preferred.
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 language 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.