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Versatile People
Versatile People

Senior/Lead Data Scientist

Location

Remote restrictions apply
See all remote locations

Salary Estimate

N/AIconOpenNewWindows

Seniority

Lead

Tech stacks

AI
Machine learning
Data
+38

Visa

U.S. visa required

Permanent role
5 months ago
Apply now

IMPORTANT INFORMATION

  • 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.

About Versatile People

👥1-10
📍London
🔗Website
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