Lead Data Scientist

Staffing Spot, Inc.; Dallas (TX); On-site; USD 150,000 - 210,000; Full time

Job summary

Staffing Spot, Inc. is seeking an experienced data science leader to drive end-to-end analytics projects from problem framing to production deployment.

You will develop ML, deep learning, and statistical models, and design Generative AI, LLM, and RAG-based solutions where applicable, translating business challenges into predictive insights. You will mentor teams, collaborate with engineering and product, and advance MLOps practices across the org.

Qualifications

• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Engineering, or a related field.
• 10 years of experience in data science, machine learning, or advanced analytics, depending on the organization.
• Strong proficiency in Python, SQL, and libraries such as pandas, scikit-learn, PyTorch, or TensorFlow.
• Expertise in supervised and unsupervised learning, feature engineering, model evaluation, and statistical methods.
• Experience deploying models using cloud platforms such as AWS, Azure, or GCP.
• Knowledge of ML pipelines, Docker, Kubernetes, CI/CD, and model monitoring.
• Strong communication, stakeholder management, and technical leadership skills.
• Strong hands-on experience building predictive machine learning models, recommender systems, ranking models, and prescriptive optimization models
• Strong proficiency in Python, SQL, MLOps, and modern AI/ML development frameworks (Scikit-learn, TensorFlow, PyTorch, Hugging Face, AWS Sagemaker, etc.)

Responsibilities

• Lead end-to-end data science projects, from problem definition to production deployment.
• Develop and optimize machine learning, deep learning, and statistical models.
• Design and implement Generative AI, LLM, and RAG-based solutions where applicable.
• Translate complex business problems into analytical and predictive solutions.
• Establish best practices for experimentation, model validation, explainability, and monitoring.
• Mentor data scientists and review technical deliverables.
• Partner with engineering, product, and business stakeholders.
• Drive MLOps practices, model governance, scalability, and responsible AI.
• Present analytical insights and recommendations to senior leadership.

Skills

Python

SQL

Pandas

Scikit-learn

PyTorch

TensorFlow

ML pipelines

Docker

Kubernetes

MLOps

AWS

Azure

GCP

Communication

Leadership

Cloud platforms

Education

Bachelor's or Master's in Computer Science, Statistics, Mathematics, Engineering, or related field

Tools

Docker

Kubernetes

CI/CD

Model monitoring

AWS

Azure

GCP

Hugging Face

Sagemaker

Job description

Key Responsibilities

• Lead end-to-end data science projects, from problem definition to production deployment.
• Develop and optimize machine learning, deep learning, and statistical models.
• Design and implement Generative AI, LLM, and RAG-based solutions where applicable.
• Translate complex business problems into analytical and predictive solutions.
• Establish best practices for experimentation, model validation, explainability, and monitoring.
• Mentor data scientists and review technical deliverables.
• Partner with engineering, product, and business stakeholders.
• Drive MLOps practices, model governance, scalability, and responsible AI.
• Present analytical insights and recommendations to senior leadership.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Engineering, or a related field.
• 10 years of experience in data science, machine learning, or advanced analytics, depending on the organization.
• Strong proficiency in Python, SQL, and libraries such as pandas, scikit-learn, PyTorch, or TensorFlow.
• Expertise in supervised and unsupervised learning, feature engineering, model evaluation, and statistical methods.
• Experience deploying models using cloud platforms such as AWS, Azure, or GCP.
• Knowledge of ML pipelines, Docker, Kubernetes, CI/CD, and model monitoring.
• Strong communication, stakeholder management, and technical leadership skills.
• Strong hands-on experience building predictive machine learning models, recommender systems, ranking models, and prescriptive optimization models
• Strong proficiency in Python, SQL, MLOps, and modern AI/ML development frameworks (Scikit-learn, TensorFlow, PyTorch, Hugging Face, AWS Sagemaker, etc.)

Skills: ml,machine learning,data science,aws,leadership,sql,data,artificial intelligence,data models,python