Senior Data Scientist

Role Description

We are seeking an accomplished Senior Data Scientist to design, develop, deploy, and optimize enterprise-grade data science and machine learning solutions that support strategic business initiatives across multiple domains. In this role, you will be responsible for the complete data science lifecycle, including:

• Translating business problems into analytical solutions
• Developing predictive models
• Deploying machine learning pipelines
• Monitoring model performance
• Supporting data-driven decision-making throughout the operational lifecycle

The successful candidate will bring deep expertise in:

• Statistical analysis
• Machine learning
• Predictive modeling
• Data engineering

You will work closely with business stakeholders, data engineers, software developers, product managers, and cross-functional teams in an Agile environment to deliver scalable, accurate, and impactful data science solutions that directly support strategic business outcomes.

Qualifications

• 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
• Hands-on experience designing, training, validating, deploying, and monitoring machine learning models
• Proven experience working with relational and NoSQL databases, large-scale datasets, data warehouses, and distributed data processing platforms
• Strong SQL skills and experience performing data exploration, feature engineering, query optimization, ETL development, data visualization, and business intelligence reporting
• Solid experience with Git-based version control workflows, CI/CD processes, MLOps practices, model deployment pipelines, and collaborative software development methodologies
• Hands-on experience deploying machine learning solutions on at least one major cloud platform (AWS, Azure, or GCP)
• Strong debugging, analytical thinking, problem-solving, and root-cause analysis skills

Requirements

• Design, build, and continuously refine scalable machine learning models, predictive analytics solutions, and statistical algorithms
• Author clean, well-documented, and production-ready analytical code
• Develop data processing pipelines for structured, semi-structured, and unstructured data
• Design and implement predictive models, recommendation systems, and anomaly detection systems
• Participate in data architecture discussions and technical strategy sessions
• Continuously evaluate and optimize model performance and pipeline efficiency
• Implement and maintain robust model lifecycle management practices
• Develop comprehensive validation frameworks
• Contribute to MLOps pipeline design and deployment automation
• Identify data quality issues and opportunities for optimization
• Collaborate effectively within Agile/Scrum delivery teams
• Maintain comprehensive technical documentation

Benefits

• Competitive base salary commensurate with experience
• Full-time, direct W2 employment
• Long-term, multi-year engagement aligned to the Bright Vision SOW delivery roadmap
• Support for H1B transfers for qualified candidates

How to Apply

For immediate consideration, please send your resume to [email protected] or contact us at View phone number on remotive.com. Learn more about Bright Vision Technologies at .

We recognize that our people are our strength, and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company.