Senior Data Scientist - Remote ML & AI for Enterprise

Bright Vision Technologies; Flower Mound (TX); Remote; USD 160,000 - 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 multiple domains.

In this role, you will lead the complete data science lifecycle—from translating business problems into analytical solutions and developing predictive models to deploying machine learning pipelines and monitoring performance.

Qualifications

• Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, AI, or related field.
• Five or more years of professional experience developing production-grade machine learning models and enterprise data science applications.
• Strong understanding of statistics, probability, ML algorithms, data modeling, feature engineering, and end-to-end ML lifecycle.

Responsibilities

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

Skills

Python

SQL

Machine learning

Statistical analysis

Data engineering

Model deployment

Big data

ETL

Education

Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, AI, or related

Tools

Scikit-learn

TensorFlow

PyTorch

Pandas

NumPy

Spark

Hadoop

Snowflake/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 multiple domains.

In this role, you will lead the complete data science lifecycle—from translating business problems into analytical solutions and developing predictive models to deploying machine learning pipelines and monitoring performance.