Senior Data Scientist

Solstice Analytics is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential. Job Title: Senior Data Scientist Location: 100% Remote (U.S.) Position Type: Full-time, Direct W2 Salary Range: $160,000–$180,000 Annually Experience Required: 6+ years Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position. 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 data 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 comprehensiv