Senior Data Scientist

Job Title: Senior Data Scientist / Machine Learning Engineer Job Location: Hybrid; 3 days onsite in New York, NY, 2 days remote Job Type: Contract, 12 months+ Pay Rate Range: $50-$58/hr W2 (depending on experience) Interview Mode: First Round remote, 2nd onsite Job Summary: Our Fortune 500 client is seeking a highly skilled Senior Data Scientist / Machine Learning Engineer to design, build, and maintain advanced analytics and machine learning solutions that drive diagnostic and predictive insights. This role blends hands-on data science, ML engineering, and MLOps, with a strong focus on developing production-ready models, ensuring data quality, and translating complex data into actionable insights for both technical and non-technical stakeholders. The ideal candidate brings deep experience in Python, SQL, PySpark, Azure Databricks, machine learning, and modern AI techniques, along with the ability to collaborate cross-functionally with Data Engineering, Analytics, and Architecture teams.Key Responsibilities Advanced Analytics & Machine Learning • Design, develop, and optimize machine learning models including forecasting, classification, and clustering solutions. • Apply data mining and statistical techniques to uncover trends, patterns, and insights from large, complex datasets. • Perform feature engineering, model training, validation, and performance tuning. • Explore and deploy modern AI, GenAI, and NLP approaches to enhance automation and analytical capabilities. • Support A/B testing and experimental design to evaluate model and business performance. Data Preparation & Quality • Prepare and analyze structured and unstructured data for advanced analytics and modeling. • Develop scripts and tools for data cleansing, validation, enrichment, and transformation. • Partner with Data Engineering to ensure efficient, reliable data pipelines and scalable data access. • Identify data quality issues, perform root cause analysis, and recommend remediation strategies. • Troubleshoot and resolve dashboard and reporting data issues to ensure accuracy and consistency. MLOps & Engineering • Design, deploy, and maintain production-grade machine learning models. • Apply MLOps best practices for model versioning, monitoring, retraining, and performance tracking. • Write high-quality, maintainable code following software engineering best practices. • Leverage cloud-based infrastructure to support scalable analytics and ML workloads. Analytics, Insights & Reporting • Conduct deep-dive analyses to support diagnostic, predictive, and prescriptive use cases. • Communicate complex analytical findings in clear, concise terms to both technical and business audiences. • Support the development of dashboards, metrics, and analytical solutions using tools such as Power BI and DAX. Cross-Functional Collaboration • Work closely with architects, engineers, analysts, and business stakeholders to define analytical requirements. • Contribute to conceptual data model design, workflow optimization, and analytics standards. • Promote best practices in data science, machine learning, analytics, and data governance. Required Qualifications • Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, or a related field. • 7+ years of experience in data science and machine learning engineering. • Strong experience with machine learning algorithms, predictive modeling, and data mining. • Proficiency in Python (required) for data science and ML workloads. • Strong SQL (required) skills with relational databases. • Proficiency in PySpark (required) for large-scale data processing. • Experience with Azure Databricks, Oracle, and modern data science libraries such as scikit-learn, pandas, and NumPy. • Experience with GenAI and large language models. • Hands-on experience with MLOps and deploying ML models into production. • Experience with Natural Language Processing (NLP). • Minimum 3 years of experience with data visualization tools such as Power BI, including DAX queries and best practices. • Strong ability to interpret complex datasets and produce actionable insights. • Excellent communication, analytical, and problem-solving skills. Preferred Qualifications • Knowledge of statistical methods and experimental design. • Experience with A/B testing frameworks. • Familiarity with cloud-native analytics and ML infrastructure.