Tavant
Overview
We are looking for an experienced
Senior Data Scientist / ML Engineer
with a strong blend of
pre-sales
expertise 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
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. 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. 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
Help lead end-to-end ML project lifecycles, from data exploration and model development to deployment and post-launch maintenance. Help 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. Required Qualifications
Education & Experience Master’s or PhD in Computer Science, Data Science, Engineering, or a related field is preferred. 6+ years
of relevant industry experience in data science or ML engineering 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). Preferred / 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. Seniority level
Mid-Senior level Employment type
Full-time Industries
Software Development
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We are looking for an experienced
Senior Data Scientist / ML Engineer
with a strong blend of
pre-sales
expertise 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
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. 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. 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
Help lead end-to-end ML project lifecycles, from data exploration and model development to deployment and post-launch maintenance. Help 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. Required Qualifications
Education & Experience Master’s or PhD in Computer Science, Data Science, Engineering, or a related field is preferred. 6+ years
of relevant industry experience in data science or ML engineering 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). Preferred / 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. Seniority level
Mid-Senior level Employment type
Full-time Industries
Software Development
#J-18808-Ljbffr