Senior Data Scientist

Bright Vision Technologies • Frisco, TX

3d • $160,000 - $180,000 • Remote

About The Position

Bright Vision Technologies is seeking an accomplished Senior Data Scientist to design, develop, deploy, and optimize enterprise-grade data science and machine learning solutions supporting strategic business initiatives across multiple domains. The role covers the complete data science lifecycle: translating business problems into analytical solutions, developing predictive models, deploying machine learning pipelines, monitoring model performance, and supporting data-driven decisions throughout operations.

The successful candidate will bring expertise in statistical analysis, machine learning, predictive modeling, and data engineering, with hands-on experience using large structured and unstructured datasets, modern analytics platforms, and cloud technologies. The role works closely with business stakeholders, data engineers, software developers, product managers, and cross-functional teams in an Agile environment to deliver scalable, accurate solutions with direct business impact. This is a full-time, direct W2 position, 100% remote in the U.S., with an annual salary range of $160,000–$180,000. The employer cannot sponsor new H-1B visa petitions; U.S. citizens, green card holders, EAD holders, and H-1B transfer candidates are encouraged to apply.

Requirements

• Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Artificial Intelligence, or a closely related quantitative discipline.
• Five or more years of professional experience developing production-grade machine learning models, predictive analytics solutions, and enterprise data science applications.
• Strong understanding of statistics, probability, machine learning algorithms, data structures, data modeling, feature engineering, model evaluation techniques, and end-to-end machine learning lifecycle principles.
• Advanced working knowledge of Python, R, SQL, Scikit-learn, TensorFlow, PyTorch, Pandas, NumPy, and modern data science libraries.
• Production-level experience designing, training, validating, deploying, and monitoring machine learning models.
• Experience with relational and NoSQL databases, large-scale datasets, data warehouses, and distributed data processing platforms such as Spark, Hadoop, Snowflake, Databricks, or BigQuery.
• Strong SQL skills and meaningful experience with data exploration, feature engineering, query optimization, ETL development, data visualization, and business intelligence reporting using enterprise data platforms.
• Experience with Git-based version control workflows, CI/CD processes, MLOps practices, model deployment pipelines, code reviews, and collaborative software development methodologies.
• Hands-on experience deploying machine learning solutions on at least one major cloud platform (AWS, Azure, or GCP), including managed AI/ML services, storage, networking, and identity management capabilities.
• Strong debugging, analytical thinking, problem-solving, and root-cause analysis skills, including the ability to communicate findings effectively and translate insights into business recommendations.

Nice To Haves

• Experience designing and deploying real-time machine learning systems, recommendation engines, streaming analytics, event-driven architectures, or large-scale AI applications using Kafka, Spark Streaming, or equivalent technologies.
• Familiarity with containerization and orchestration using Docker, Kubernetes, Kubeflow, MLflow, Airflow, or equivalent platforms.
• Exposure to deep learning, reinforcement learning, computer vision, generative AI, large language models (LLMs), explainable AI (XAI), model fairness, and responsible AI practices.
• Experience implementing automated testing, model monitoring, feature stores, experiment tracking, data governance, MLOps best practices, and continuous machine learning delivery pipelines in enterprise Agile software development environments.

Responsibilities

• Design, develop, deploy, and optimize enterprise-grade data science and machine learning solutions.
• Translate business problems into analytical solutions and develop predictive models.
• Deploy machine learning pipelines, monitor model performance, and support data-driven decision-making throughout the operational lifecycle.
• Design, train, validate, deploy, and monitor machine learning models, including regression, classification, clustering, forecasting, recommendation systems, and natural language processing applications.
• Work with large-scale structured and unstructured datasets using modern analytics platforms and cloud technologies.
• Collaborate with business stakeholders, data engineers, software developers, product managers, and cross-functional teams in an Agile environment.
• Perform data exploration, feature engineering, query optimization, ETL development, data visualization, and business intelligence reporting.
• Investigate complex data challenges, communicate findings, and translate analytical insights into actionable business recommendations.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior