Note: The job is a remote job and is open to candidates in USA. Bright Vision Technologies is seeking a Senior Data Scientist to build and optimize enterprise data science and machine learning solutions supporting strategic business initiatives. The role covers the data science lifecycle and involves collaborating with stakeholders and cross-functional teams to deliver scalable solutions and support data-driven decisions.
Responsibilities
- 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, from translating business problems into analytical solutions and developing predictive models to deploying machine learning pipelines, monitoring model performance, and supporting data-driven decision-making throughout the operational lifecycle
- 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
Skills
- U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply
- 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, demonstrable 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 for building scalable analytical solutions
- Hands-on, production-level experience designing, training, validating, deploying, and monitoring machine learning models, including regression, classification, clustering, forecasting, recommendation systems, and natural language processing applications
- Proven experience working 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 performing data exploration, feature engineering, query optimization, ETL development, data visualization, and business intelligence reporting using enterprise data platforms
- Solid experience with Git-based version control workflows, CI/CD processes, MLOps practices, model deployment pipelines, code review processes, 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, with the discipline to investigate complex data challenges methodically, communicate findings effectively, and translate analytical insights into actionable business recommendations
- 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 for production machine learning operations
- Exposure to advanced artificial intelligence concepts such as 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 within enterprise Agile software development environments
Benefits
- 100% Remote (U.S.)
- Tremendous career growth potential
Company Overview