Senior Data Scientist

Bright Vision Technologies • Overland Park, KS

2d • $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 spans the full data science lifecycle, including translating business problems into analytical solutions, developing predictive models, deploying machine learning pipelines, monitoring model performance, and supporting data-driven decision-making throughout operations.

The successful candidate will bring expertise in statistical analysis, machine learning, predictive modeling, and data engineering, along with hands-on experience working with large structured and unstructured datasets using modern analytics platforms and cloud technologies. The Senior Data Scientist will collaborate with business stakeholders, data engineers, software developers, product managers, and cross-functional teams in an Agile environment to deliver scalable, accurate, and impactful solutions aligned with strategic business outcomes.

This is a full-time, direct W2 position that is 100% remote within the United States. The stated annual salary range is $160,000–$180,000. Bright Vision Technologies is unable to sponsor new H-1B visa petitions for this position, but 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, including regression, classification, clustering, forecasting, recommendation systems, and natural language processing applications.
• 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 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 review, 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 and translate insights into actionable 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 and monitor model performance.
• Support data-driven decision-making throughout the operational lifecycle.
• 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.
• Deliver scalable, accurate, and impactful data science solutions that support strategic business outcomes.

What This Job Offers

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