Senior Data Scientist — Remote (US) Growth Opportunity

Bright Vision Technologies; Chesterfield (MO); Remote; USD 108,000 - 180,000; Full time

Job summary

Bright Vision Technologies is seeking a Senior Data Scientist to design, develop, deploy, and optimize enterprise-grade data science and ML solutions that support strategic business initiatives across domains.

The role covers the complete data science lifecycle from problem framing to model deployment, monitoring, and data-driven decision making, with collaboration across data engineers, software developers, product managers, and stakeholders in an Agile setting.

Qualifications

• 6+ years of professional experience in data science and ML.
• Strong knowledge of statistics, probability, ML algorithms, data structures, and end-to-end ML lifecycle principles.
• Advanced working knowledge of Python, R, SQL and ML libraries for scalable analytics.
• Hands-on experience deploying ML models and monitoring performance.
• Experience with relational and NoSQL databases and distributed data processing platforms.

Responsibilities

• Design, develop, deploy, and optimize enterprise-grade data science and ML solutions.
• Translate business problems into analytical solutions and predictive models.
• Deploy ML pipelines, monitor performance, and support data-driven decision-making.
• Collaborate with stakeholders, data engineers, software developers, product managers, and cross-functional teams in an Agile environment to deliver scalable ML solutions.

Skills

Python

SQL

Machine Learning

Data Engineering

Cloud Platforms

Education

Bachelor's degree in Data Science

Tools

TensorFlow

PyTorch

Scikit-learn

Pandas

NumPy

Spark

Hadoop

Snowflake

Databricks

BigQuery

Job description

Bright Vision Technologies is seeking a Senior Data Scientist to design, develop, deploy, and optimize enterprise-grade data science and ML solutions that support strategic business initiatives across domains.

The role covers the complete data science lifecycle from problem framing to model deployment, monitoring, and data-driven decision making, with collaboration across data engineers, software developers, product managers, and stakeholders in an Agile setting.