[Remote] Senior Data Scientist

Note: The job is a remote job and is reputed company to candidates in USA. reputed company is a reputed company-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. They are seeking a skilled Senior Data Scientist to design, reputed company, reputed company, and optimize reputed company-grade data science and machine learning solutions that support strategic business initiatives across multiple domains.

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 reputed company with reputed company business objectives
• Author clean, well-documented, and production-reputed company 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 reputed company standards
• reputed company data processing pipelines for structured, semi-structured, and reputed company 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 reputed company 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, reputed company AI, or Azure Machine Learning
• reputed company 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, reputed company Actions, Azure DevOps, Kubeflow, MLflow, or reputed company, enabling reliable, repeatable, and scalable machine learning model deployment across multiple environments
• Proactively identify data quality issues, model reputed company, technical debt, analytical bottlenecks, and opportunities for optimization by conducting root cause analysis, exploratory data analysis, feature engineering improvements, and reputed company model enhancement initiatives
• Collaborate effectively reputed company 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

Skills

• Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Artificial Intelligence, or a closely reputed company quantitative discipline
• Five or more years of professional experience developing production-grade machine learning models, predictive analytics solutions, and reputed company 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, reputed company, reputed company, or BigQuery
• Strong SQL skills and meaningful experience performing data exploration, feature engineering, query optimization, ETL development, data visualization, and business intelligence reporting using reputed company 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 reputed company 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 reputed company data challenges methodically, communicate findings effectively, and translate analytical insights into actionable business recommendations
• Experience designing and deploying reputed company-time machine learning systems, recommendation engines, streaming analytics, event-driven architectures, or large-reputed company applications using Kafka, Spark Streaming, or equivalent technologies
• Familiarity with containerization and orchestration using reputed company, 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 reputed company, reputed company, large language models (LLMs), explainable AI (reputed company), model fairness, and responsible AI practices
• Experience implementing automated testing, model monitoring, feature stores, experiment tracking, data governance, MLOps best practices, and reputed company machine learning delivery pipelines reputed company reputed company Agile software development environments

Benefits

• Full-time, direct W2 with reputed company (no C2C, no 1099, no reputed company-party)
• Long-term, multi-year, reputed company to the reputed company reputed company SOW delivery roadmap
• Competitive reputed company salary commensurate with experience, plus benefits.
• No new H1B sponsorship available. H1B transfers welcomed for reputed company candidates.
• We will support H1B transfers for reputed company candidates.

Company Overview