Senior Data Scientist

Senior Data Scientist

Bright Vision Technologies is a forward-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. We leverage cutting-edge technologies to create scalable, secure, and user-friendly applications.

As we continue to grow, we’re looking for a skilled Senior Data Scientist to join our dynamic team and contribute to our mission of transforming business processes through technology. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.

Job Summary

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. The successful candidate will bring deep expertise in statistical analysis, machine learning, predictive modeling, and data engineering, combined with strong hands-on experience working with large-scale structured and unstructured datasets using modern analytics platforms and cloud technologies. 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.

Key Responsibilities

• Design, build, and continuously refine scalable machine learning models, predictive analytics solutions, and statistical algorithms using Python, R, SQL, and modern machine learning frameworks, ensuring models are accurate, explainable, maintainable, and aligned with enterprise business objectives.
• Author clean, well-documented, and production-ready analytical code that follows established software engineering best practices, incorporates robust data validation, feature engineering, model versioning, and reproducible workflows while ensuring compliance with organizational governance and security standards.
• Develop data processing pipelines for structured, semi-structured, and unstructured data using Python, SQL, Spark, or equivalent technologies, enabling efficient data ingestion, transformation, feature extraction, and preparation for advanced analytics and machine learning workloads.
• Design and implement predictive models, recommendation systems, forecasting solutions, classification algorithms, clustering models, natural language processing (NLP), and anomaly detection systems that integrate seamlessly with enterprise applications and business processes.
• Actively participate in data architecture discussions, model design reviews, business requirement workshops, and technical strategy sessions by providing analytical insights, evaluating modeling approaches, and recommending scalable, data-driven solutions that balance accuracy, interpretability, and operational efficiency.
• Continuously evaluate and optimize model performance, feature selection, hyperparameter tuning, data quality, pipeline efficiency, and inference latency by leveraging statistical techniques, cross-validation, performance monitoring, and model retraining strategies.
• Implement and maintain robust model lifecycle management practices including experiment tracking, feature stores, model registry, version control, automated retraining, monitoring, explainability, and governance using platforms such as MLflow, SageMaker, Vertex AI, or Azure Machine Learning.
• Develop comprehensive validation frameworks including unit testing for data pipelines, model validation, performance benchmarking, bias detection, fairness analysis, and production monitoring while utilizing frameworks such as Scikit-learn, TensorFlow, PyTorch, Pandas, and Great Expectations.
• Contribute meaningfully to MLOps pipeline design and deployment automation using tools such as Jenkins, GitHub Actions, Azure DevOps, Kubeflow, MLflow, or Docker, enabling reliable, repeatable, and scalable machine learning model deployment across multiple environments.
• Proactively identify data quality issues, model drift, technical debt, analytical bottlenecks, and opportunities for optimization by conducting root cause analysis, exploratory dataAn analysis, feature engineering improvements, and continuous model enhancement initiatives.
• Collaborate effectively within Agile/Scrum delivery teams, participating in sprint planning, daily standups, backlog refinement, model demonstrations, retrospectives, and cross-functional knowledge-sharing sessions to ensure timely delivery of high-value analytical solutions.
• Maintain comprehensive technical documentation—including data dictionaries, feature engineering documentation, model specifications, validation reports, deployment guides, experiment logs, and operational runbooks—so that analytical solutions remain transparent, reproducible, and maintainable as the organization scales.

Required 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, 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.

Preferred Qualifications

• 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.

Key skills/competency

• Data Science
• Machine Learning
• Python
• SQL
• Statistical Analysis
• Predictive Modeling
• Data Engineering
• Cloud Platforms (AWS, Azure, GCP)
• MLOps
• Agile Methodologies