Lead Data Scientist

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Position: Senior Lead Data Scientist

Location: USA - Chicago, Dallas, Atlanta, NY, Santa Clara

Experience Required: 12 - 18 Years

Total Experience Requirement - 12-18 Years

A minimum of 5 years in Python, AI technologies, Neural Networks, Natural Language Processing, Computer Vision, alongside machine learning and data science experience is essential.

Experience in Pre-Sales is crucial.

The ability to articulate ideas and express them clearly is a must.

We seek an accomplished Senior Lead Data Scientist / ML Engineer who possesses a unique combination of pre-sales experience, team leadership skills, and technical acumen in classical machine learning, deep learning, and generative AI. Your role will involve high-stakes client engagements, formulating technical sales approaches, and steering a team to create and deliver advanced ML solutions. This pivotal role calls for both strategic insight and hands-on technical involvement.

Responsibilities

• Client Engagement & Pre-Sales
• Work closely with sales and business development teams to understand client requirements and design AI/ML solutions.
• Deliver presentations on technical concepts, project plans, and proofs of concept (POCs) to potential clients.
• Convert complex client needs into practical project outlines, estimates, and detailed proposals.
• Team Leadership & Management
• Guide, mentor, and provide constructive feedback to a group of data scientists and ML engineers.
• Uphold best practices in solution creation, code assessment, model validation, and production-based deployments.
• Define the strategic direction for AI projects, ensuring they align with organizational aims and market dynamics.
• Classical Machine Learning & Statistical Modeling
• Utilize classical ML methods (such as regression, clustering, decision trees, and ensemble techniques) to tackle various business challenges.
• Construct and refine data pipelines, feature creation processes, and model selection tactics.
• Facilitate thorough model evaluation, tuning, and performance tracking within production settings.
• Deep Learning & Generative AI
• Create and sustain deep learning models with tools like TensorFlow or PyTorch for applications in computer vision, NLP, or recommendation systems.
• Investigate and develop solutions using generative AI methodologies (like GANs, VAEs, or transformers) for novel product functionalities and services.
• Promote research and experimentation with cutting-edge AI frameworks, remaining at the forefront of industry developments.
• Project Implementation & MLOps
• Oversee the complete lifecycle of ML projects, from data analysis and model crafting to deployment and ongoing maintenance.
• Apply MLOps best practices (such as CI/CD, containerization, and version control) across cloud or on-prem infrastructures.
• Partner with DevOps and engineering teams to integrate ML solutions harmoniously within existing architectures.
• Stakeholder Interaction & Communication
• Act as a primary technical consultant for senior leadership, product managers, and client-facing teams.
• Clearly convey intricate AI/ML insights in digestible terms for both technical and non-technical stakeholders.
• Champion a data-driven approach to decision-making and promote a culture of innovation throughout the organization.
  
  

Qualifications

• Education & Experience
• A Master’s or PhD in Computer Science, Data Science, Engineering, or a relevant discipline is preferred.
• At least 12 years of pertinent industry involvement in data science or ML engineering, with 5 years in leadership or management roles.
• Technical Skills
• Pre-Sales: Proven experience in client-facing engagements, solution development, and proposal creation.
• Classical ML: Proficient in conventional algorithms (e.g., regression, classification, clustering) and statistical techniques.
• Deep Learning: Practical experience with frameworks (like TensorFlow or PyTorch) applicable to CNNs, RNNs, and transformer structures.
• Generative AI: Hands-on experience with GANs, VAEs, or large language models, with a proven history of constructing generative solutions.
• MLOps: Knowledge of CI/CD methodologies, Docker/Kubernetes systems, and cloud services (AWS, Azure, GCP).
• Leadership & Communication
• Demonstrated ability to guide and manage data science/ML engineering teams to achieve project objectives.
• Outstanding communication skills for effective presentations to clients, stakeholders, and executive teams.
• Familiarity with agile methodologies and project management, managing multiple projects concurrently.
  
  

Extra Skills (Preferred)

• Familiarity with big data technologies (Spark, Hadoop) for high-volume data processing.
• Experience in NLP, computer vision, or recommendation systems.
• Understanding of DevOps tools (Jenkins, GitLab CI, Terraform) for automation of infrastructure.
• Record of published research or contributions to open-source AI initiatives.
  
  

Skills: data science,devops,big data ecosystems,cloud platforms (aws, azure, gcp),machine learning,generative ai,aws,pytorch,kubernetes,neural networks,python,natural language processing,mlops,hadoop,statistical modeling,big data,agile methodologies,devops tools,classical machine learning,spark,recommendation systems,devops tools (jenkins, gitlab ci, terraform),artificial intelligence,artificial neural networks,ml,tensorflow,pre-sales,project,big data ecosystems (spark, hadoop),deep learning,azure,nlp,project management,ci/cd,docker,pre sales,gcp,computer vision,classical ml